diff --git a/.gitignore b/.gitignore index 474decf31..8959d6062 100644 --- a/.gitignore +++ b/.gitignore @@ -117,4 +117,15 @@ double_ml/proto/*.jpg doc/_autosummary/ # compiled C files -*.c \ No newline at end of file +*.c + +# local knowledge graph / repo understanding artifacts +.understand-anything/ + +# local accidental artifacts / unrelated files +.DS_Store +Thumbs.db +*.swp +*.swo +*.orig +forest.jbl diff --git a/doc/reference.rst b/doc/reference.rst index e46a67414..d47e70e1a 100644 --- a/doc/reference.rst +++ b/doc/reference.rst @@ -56,6 +56,50 @@ Orthogonal Random Forest (ORF) econml.orf.DMLOrthoForest econml.orf.DROrthoForest +.. _censored_outcomes_api: + +Censored Outcomes +^^^^^^^^^^^^^^^^^ + +.. autosummary:: + :toctree: _autosummary + + econml.censor.fit_nuisance_survival + econml.censor.fit_nuisance_survival_crossfit + econml.censor.fit_nuisance_competing_crossfit + econml.censor.NuisanceResult + econml.censor.CrossFitNuisanceResult + econml.censor.ipcw_cut_rmst + econml.censor.bj_cut_rmst + econml.censor.aipcw_cut_rmst + econml.censor.uif_diff_rmst + econml.censor.ipcw_cut_rmtlj + econml.censor.bj_cut_rmtlj + econml.censor.aipcw_cut_rmtlj + econml.censor.aipcw_cut_rmtlj_sep_direct_astar1 + econml.censor.aipcw_cut_rmtlj_sep_indirect_astar1 + econml.censor.uif_diff_rmtlj + econml.censor.uif_diff_rmtlj_sep_direct_astar1 + econml.censor.uif_diff_rmtlj_sep_indirect_astar1 + econml.metalearners.SurvivalTLearner + econml.metalearners.SurvivalSLearner + econml.metalearners.CompetingRisksTLearner + econml.metalearners.CompetingRisksSLearner + econml.metalearners.TLearner + econml.metalearners.SLearner + econml.metalearners.XLearner + econml.metalearners.IPTWLearner + econml.metalearners.AIPTWLearner + econml.metalearners.MCLearner + econml.metalearners.MCEALearner + econml.metalearners.ULearner + econml.metalearners.RALearner + econml.metalearners.RLearner + econml.metalearners.IFLearner + econml.grf.CausalSurvivalForest + econml.grf.GRFCausalForest + econml.grf.causal_forest + Instrumental Variable CATE Estimators ------------------------------------- diff --git a/doc/spec/estimation.rst b/doc/spec/estimation.rst index 8b5c02594..932857c60 100644 --- a/doc/spec/estimation.rst +++ b/doc/spec/estimation.rst @@ -12,3 +12,5 @@ had a direct effect on the treatment decision in the collected data and the obse estimation/dr.rst estimation/forest.rst estimation/metalearners.rst + estimation/survival.rst + estimation/competing_risks.rst diff --git a/doc/spec/estimation/competing_risks.rst b/doc/spec/estimation/competing_risks.rst new file mode 100644 index 000000000..2c1d75758 --- /dev/null +++ b/doc/spec/estimation/competing_risks.rst @@ -0,0 +1,260 @@ +.. _competingrisksuserguide: + +================================ +Competing Risks HTE Estimation +================================ + +What is it? +=========== + +This section documents the censored-outcome estimators for competing-risks +targets that were added for this project. The primary estimand is the +restricted mean time lost (RMTL) for a target cause :math:`j` up to a horizon +:math:`\tau`. The package also includes separable direct and indirect effect +transformations for the competing-risks setting. + +The implementation follows the migration from the R reference code in +``original/learners/competing_cut.R`` and ``original/learners/hte_learners.R``. +As in the survival setting, nuisance quantities and learner predictions are +computed out of sample. + + +What are the estimands? +======================= + +For a target cause :math:`j`, the primary total-effect estimand is the +conditional RMTL treatment effect up to horizon :math:`\tau`: + +.. math:: + + \psi_{j,0}^{\mathrm{RMTL}}(\tau, X) + = + E_{0}[\{\tau - \min(T^{a=1}, \tau)\} I(J^{a=1}=j) \mid X] + - + E_{0}[\{\tau - \min(T^{a=0}, \tau)\} I(J^{a=0}=j) \mid X]. + +Equivalently, if :math:`\mathrm{RMTL}_{j,0}(\tau \mid a, X)` denotes the +arm-specific conditional restricted mean time lost for cause :math:`j`, then + +.. math:: + + \psi_{j,0}^{\mathrm{RMTL}}(\tau, X) + = + \mathrm{RMTL}_{j,0}(\tau \mid 1, X) + - + \mathrm{RMTL}_{j,0}(\tau \mid 0, X). + +The total-effect transformations +:func:`~econml.censor.ipcw_cut_rmtlj`, +:func:`~econml.censor.bj_cut_rmtlj`, +:func:`~econml.censor.aipcw_cut_rmtlj`, and +:func:`~econml.censor.uif_diff_rmtlj` all target this same conditional RMTL +contrast. + +The notebook and API also expose separable direct and indirect effects. These +are defined by decomposing treatment into :math:`A_1` and :math:`A_2`, where +:math:`A_1` acts directly on the event of interest and :math:`A_2` acts +indirectly through the competing event. + +The separable direct RMTL effect is + +.. math:: + + \psi_{j,0}^{\mathrm{RMTL}, \mathrm{sep-D}}(\tau, a_2, X) + = + \mathrm{RMTL}_{j,0}(\tau \mid 1, a_2, X) + - + \mathrm{RMTL}_{j,0}(\tau \mid 0, a_2, X), + +and the separable indirect RMTL effect is + +.. math:: + + \psi_{j,0}^{\mathrm{RMTL}, \mathrm{sep-I}}(\tau, a_1, X) + = + \mathrm{RMTL}_{j,0}(\tau \mid a_1, 1, X) + - + \mathrm{RMTL}_{j,0}(\tau \mid a_1, 0, X). + +The current notebook workflows and transformation helpers with suffix +``astar1`` use the case :math:`a^{\star}=1`, i.e. direct effects at +:math:`a_2=1` and indirect effects at :math:`a_1=1`. + + +What are the nuisance quantities? +================================= + +For a target cause :math:`j`, the competing-risks workflow uses: + +* :math:`G(t \mid A, X)`: censoring survival +* :math:`S(t \mid A, X)`: overall event survival +* :math:`S_j(t \mid A, X)`: cause-:math:`j` survival component +* :math:`\bar{S}_j(t \mid A, X)`: competing-cause survival component +* :math:`e(X) = P(A=1 \mid X)`: treatment propensity, when needed for UIF scores + +These are estimated through +:func:`~econml.censor.fit_nuisance_competing_crossfit`. + + +How is the event variable defined? +================================== + +In the competing-risks setting, the structured outcome ``Y`` again uses the +fields ``time`` and ``event``, but the event field is multi-valued: + +* ``event == 0``: censored observation +* ``event == j``: target cause :math:`j` +* ``event != 0``: any failure event +* ``event != 0`` and ``event != j``: a competing failure cause + +For the common case ``cause=1``, this becomes: + +* ``event == 0``: censored +* ``event == 1``: target failure cause +* ``event > 1``: competing failure cause + +This coding is used by the nuisance functions as follows: + +* :math:`G(t \mid A, X)` uses ``event == 0`` +* :math:`S(t \mid A, X)` uses ``event != 0`` +* :math:`S_j(t \mid A, X)` uses ``event == j`` +* :math:`\bar{S}_j(t \mid A, X)` uses ``event != 0`` and ``event != j`` + +The same convention is used in the simulation and bone marrow example +notebooks for relapse-vs-NRM analyses. + + +Which transformations are available? +==================================== + +Total-effect transformations +---------------------------- + +* :func:`~econml.censor.ipcw_cut_rmtlj` +* :func:`~econml.censor.bj_cut_rmtlj` +* :func:`~econml.censor.aipcw_cut_rmtlj` +* :func:`~econml.censor.uif_diff_rmtlj` + +Separable-effect transformations +-------------------------------- + +* :func:`~econml.censor.aipcw_cut_rmtlj_sep_direct_astar1` +* :func:`~econml.censor.aipcw_cut_rmtlj_sep_indirect_astar1` +* :func:`~econml.censor.uif_diff_rmtlj_sep_direct_astar1` +* :func:`~econml.censor.uif_diff_rmtlj_sep_indirect_astar1` + + +Which estimators are available? +=============================== + +Direct competing-risks learners +------------------------------- + +The following classes operate directly on a structured competing-risks outcome +object ``Y`` with fields ``event`` and ``time``: + +* :class:`~econml.metalearners.CompetingRisksTLearner` +* :class:`~econml.metalearners.CompetingRisksSLearner` + +The following dedicated direct learners target the separable ``astar1`` +estimands directly: + +* :class:`~econml.metalearners.SeparableDirectAstar1TLearner` +* :class:`~econml.metalearners.SeparableDirectAstar1SLearner` +* :class:`~econml.metalearners.SeparableIndirectAstar1TLearner` +* :class:`~econml.metalearners.SeparableIndirectAstar1SLearner` + +Pseudo-outcome learners +----------------------- + +After constructing an RMTL pseudo-outcome, the following cross-fitted learners +can be used: + +* :class:`~econml.metalearners.TLearner` +* :class:`~econml.metalearners.SLearner` +* :class:`~econml.metalearners.XLearner` +* :class:`~econml.metalearners.IPTWLearner` +* :class:`~econml.metalearners.AIPTWLearner` +* :class:`~econml.metalearners.MCLearner` +* :class:`~econml.metalearners.MCEALearner` +* :class:`~econml.metalearners.ULearner` +* :class:`~econml.metalearners.RALearner` +* :class:`~econml.metalearners.RLearner` +* :class:`~econml.metalearners.IFLearner` +* :class:`~econml.grf.GRFCausalForest` + + +Recommended workflow +==================== + +The intended workflow is: + +1. Estimate competing-risks nuisance functions with + :func:`~econml.censor.fit_nuisance_competing_crossfit`. +2. Construct a total-effect or separable-effect pseudo-outcome. +3. Fit a direct competing-risks learner or a generic cross-fitted learner on + the pseudo-outcome. + +This gives a unified way to compare direct competing-risks learners against +transformed-outcome learners in the same notebook workflow. + +The public ``econml.metalearners`` names listed here resolve to the +cross-fitted censored-outcome implementations, so training-sample predictions +are returned as fold-held-out out-of-sample estimates rather than in-sample +fits. + + +Notebook examples +================= + +The following notebooks demonstrate the competing-risks workflow: + +* `Competing Risks HTE Examples.ipynb `_ +* `Censored Outcomes - Bone Marrow Transplant.ipynb `_ + + +API summary +=========== + +Utilities and transformations +----------------------------- + +.. autosummary:: + :toctree: ../../_autosummary + + econml.censor.fit_nuisance_competing_crossfit + econml.censor.CrossFitNuisanceResult + econml.censor.ipcw_cut_rmtlj + econml.censor.bj_cut_rmtlj + econml.censor.aipcw_cut_rmtlj + econml.censor.aipcw_cut_rmtlj_sep_direct_astar1 + econml.censor.aipcw_cut_rmtlj_sep_indirect_astar1 + econml.censor.uif_diff_rmtlj + econml.censor.uif_diff_rmtlj_sep_direct_astar1 + econml.censor.uif_diff_rmtlj_sep_indirect_astar1 + +Learners +-------- + +.. autosummary:: + :toctree: ../../_autosummary + + econml.metalearners.CompetingRisksTLearner + econml.metalearners.CompetingRisksSLearner + econml.metalearners.SeparableDirectAstar1TLearner + econml.metalearners.SeparableDirectAstar1SLearner + econml.metalearners.SeparableIndirectAstar1TLearner + econml.metalearners.SeparableIndirectAstar1SLearner + econml.metalearners.TLearner + econml.metalearners.SLearner + econml.metalearners.XLearner + econml.metalearners.IPTWLearner + econml.metalearners.AIPTWLearner + econml.metalearners.MCLearner + econml.metalearners.MCEALearner + econml.metalearners.ULearner + econml.metalearners.RALearner + econml.metalearners.RLearner + econml.metalearners.IFLearner + econml.grf.GRFCausalForest + econml.grf.causal_forest diff --git a/doc/spec/estimation/survival.rst b/doc/spec/estimation/survival.rst new file mode 100644 index 000000000..17aec60cd --- /dev/null +++ b/doc/spec/estimation/survival.rst @@ -0,0 +1,203 @@ +.. _survivaluserguide: + +======================== +Survival HTE Estimation +======================== + +What is it? +=========== + +This section documents the censored-outcome estimators for single-event survival +targets that were added for this project. The implemented estimand is the +restricted mean survival time (RMST) contrast up to a user-specified horizon +:math:`\tau`. + +The implementation follows the migration from the R reference code in +``original/learners/survival_cut.R`` and ``original/learners/hte_learners.R``. +The main design choices in this project are: + +* nuisance quantities are estimated out of sample +* learner predictions on the training sample are also out of sample +* survival-specific nuisances are handled through ``econml.censor`` +* second-stage HTE estimation is handled through cross-fitted learner classes + + +What is the estimand? +===================== + +The main estimand in the single-event survival setting is the conditional RMST +treatment effect up to horizon :math:`\tau`: + +.. math:: + + \psi_{0}^{\mathrm{RMST}}(\tau, X) + = + E_{0}\{\min(T^{a=1}, \tau) \mid X\} + - + E_{0}\{\min(T^{a=0}, \tau) \mid X\}. + +Equivalently, if :math:`\mathrm{RMST}_{0}(\tau \mid a, X)` denotes the arm- +specific conditional restricted mean survival time, then + +.. math:: + + \psi_{0}^{\mathrm{RMST}}(\tau, X) + = + \mathrm{RMST}_{0}(\tau \mid 1, X) + - + \mathrm{RMST}_{0}(\tau \mid 0, X). + +The transformations :func:`~econml.censor.ipcw_cut_rmst`, +:func:`~econml.censor.bj_cut_rmst`, +:func:`~econml.censor.aipcw_cut_rmst`, and +:func:`~econml.censor.uif_diff_rmst` are all designed so that downstream +continuous-outcome learners target this same conditional RMST contrast. + + +What are the core building blocks? +================================== + +Single-cause survival estimation in the package is organized into four layers: + +* nuisance estimation in :mod:`econml.censor` +* RMST pseudo-outcome transformations in :mod:`econml.censor` +* direct survival learners in :mod:`econml.metalearners` +* forest-based estimators in :mod:`econml.grf` + +The survival nuisance functions are: + +* :math:`G(t \mid A, X)`: censoring survival +* :math:`S(t \mid A, X)`: event survival +* :math:`e(X) = P(A=1 \mid X)`: treatment propensity, when needed for UIF-style scores + +These nuisance quantities are combined into the following transformations: + +* :func:`~econml.censor.ipcw_cut_rmst` +* :func:`~econml.censor.bj_cut_rmst` +* :func:`~econml.censor.aipcw_cut_rmst` +* :func:`~econml.censor.uif_diff_rmst` + + +How is the event variable defined? +================================== + +In the single-event survival setting, the structured outcome ``Y`` uses two +fields: ``time`` and ``event``. The event coding is: + +* ``event == 0``: censored observation +* ``event != 0``: failure event + +This event coding is used consistently by the nuisance functions: + +* :math:`G(t \mid A, X)` is the censoring survival function, so its event + indicator is ``event == 0`` +* :math:`S(t \mid A, X)` is the failure-event survival function, so its event + indicator is ``event != 0`` + +The same convention is used in the simulation and bone marrow example +notebooks for the single-cause survival endpoints. + + +Which estimators are available? +=============================== + +Direct survival learners +------------------------ + +The following classes operate directly on a structured survival outcome object +``Y`` with fields ``event`` and ``time``: + +* :class:`~econml.metalearners.SurvivalTLearner` +* :class:`~econml.metalearners.SurvivalSLearner` +* :class:`~econml.grf.CausalSurvivalForest` + +Pseudo-outcome learners +----------------------- + +After constructing an RMST pseudo-outcome, the following cross-fitted learners +can be used: + +* :class:`~econml.metalearners.TLearner` +* :class:`~econml.metalearners.SLearner` +* :class:`~econml.metalearners.XLearner` +* :class:`~econml.metalearners.IPTWLearner` +* :class:`~econml.metalearners.AIPTWLearner` +* :class:`~econml.metalearners.MCLearner` +* :class:`~econml.metalearners.MCEALearner` +* :class:`~econml.metalearners.ULearner` +* :class:`~econml.metalearners.RALearner` +* :class:`~econml.metalearners.RLearner` +* :class:`~econml.metalearners.IFLearner` +* :class:`~econml.grf.GRFCausalForest` + + +Recommended workflow +==================== + +The intended workflow is: + +1. Estimate nuisance functions with + :func:`~econml.censor.fit_nuisance_survival_crossfit` or + :func:`~econml.censor.fit_nuisance_survival`. +2. Construct the target pseudo-outcome with one of the RMST transformations. +3. Fit a cross-fitted learner on ``(Y^*, T, X)``. + +The public ``econml.metalearners`` names listed here resolve to the +cross-fitted censored-outcome implementations, so training-sample predictions +are returned as fold-held-out out-of-sample estimates rather than in-sample +fits. + +This mirrors the two-stage orthogonal-learning structure used throughout +EconML, except that the censored-outcome transformation is made explicit as a +separate survival-specific step. + + +Notebook examples +================= + +The following notebooks demonstrate the survival workflow: + +* `Survival HTE Examples.ipynb `_ +* `Censored Outcomes - Bone Marrow Transplant.ipynb `_ + + +API summary +=========== + +Utilities and transformations +----------------------------- + +.. autosummary:: + :toctree: ../../_autosummary + + econml.censor.fit_nuisance_survival + econml.censor.fit_nuisance_survival_crossfit + econml.censor.NuisanceResult + econml.censor.CrossFitNuisanceResult + econml.censor.ipcw_cut_rmst + econml.censor.bj_cut_rmst + econml.censor.aipcw_cut_rmst + econml.censor.uif_diff_rmst + +Learners +-------- + +.. autosummary:: + :toctree: ../../_autosummary + + econml.metalearners.SurvivalTLearner + econml.metalearners.SurvivalSLearner + econml.metalearners.TLearner + econml.metalearners.SLearner + econml.metalearners.XLearner + econml.metalearners.IPTWLearner + econml.metalearners.AIPTWLearner + econml.metalearners.MCLearner + econml.metalearners.MCEALearner + econml.metalearners.ULearner + econml.metalearners.RALearner + econml.metalearners.RLearner + econml.metalearners.IFLearner + econml.grf.CausalSurvivalForest + econml.grf.GRFCausalForest + econml.grf.causal_forest diff --git a/econml/censor/__init__.py b/econml/censor/__init__.py new file mode 100644 index 000000000..629f56f54 --- /dev/null +++ b/econml/censor/__init__.py @@ -0,0 +1,47 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +"""Survival analysis utilities for HTE estimation with censored outcomes.""" + +from ._transformations import ( + ipcw_cut_rmst, + bj_cut_rmst, + aipcw_cut_rmst, + uif_diff_rmst, + ipcw_cut_rmtlj, + bj_cut_rmtlj, + aipcw_cut_rmtlj, + aipcw_cut_rmtlj_sep_direct_astar1, + aipcw_cut_rmtlj_sep_indirect_astar1, + uif_diff_rmtlj, + uif_diff_rmtlj_sep_direct_astar1, + uif_diff_rmtlj_sep_indirect_astar1, +) + +from ._nuisance import ( + fit_nuisance_survival, + fit_nuisance_survival_crossfit, + fit_nuisance_competing_crossfit, + NuisanceResult, + CrossFitNuisanceResult, +) + +__all__ = [ + "ipcw_cut_rmst", + "bj_cut_rmst", + "aipcw_cut_rmst", + "uif_diff_rmst", + "ipcw_cut_rmtlj", + "bj_cut_rmtlj", + "aipcw_cut_rmtlj", + "aipcw_cut_rmtlj_sep_direct_astar1", + "aipcw_cut_rmtlj_sep_indirect_astar1", + "uif_diff_rmtlj", + "uif_diff_rmtlj_sep_direct_astar1", + "uif_diff_rmtlj_sep_indirect_astar1", + "fit_nuisance_survival", + "fit_nuisance_survival_crossfit", + "fit_nuisance_competing_crossfit", + "NuisanceResult", + "CrossFitNuisanceResult", +] diff --git a/econml/censor/_nuisance.py b/econml/censor/_nuisance.py new file mode 100644 index 000000000..1b51ff570 --- /dev/null +++ b/econml/censor/_nuisance.py @@ -0,0 +1,513 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +""" +Nuisance survival model fitting utilities. + +Fits per-arm survival models for censoring G(t|a,X), overall event S(t|a,X), +cause-j S_j(t|a,X), and competing-cause S_jbar(t|a,X), then evaluates them +on a common time grid to produce (n, ns) matrices ready for CUT transforms. + +This is a standalone utility — it does not depend on any specific learner. +Users can feed the resulting matrices into: + +- CUT transforms in ``econml.censor`` (ipcw, bj, aipcw, uif) +- Metalearners in ``econml.metalearners`` +- ``CausalSurvivalForest`` in ``econml.grf`` +- Any custom analysis +""" + +import warnings + +import numpy as np +from sklearn import clone +from sklearn.ensemble import RandomForestClassifier +from sksurv.ensemble import RandomSurvivalForest + + +_PROPENSITY_CLIP = 1e-3 +_DEFAULT_NUISANCE_RANDOM_STATE = 123 + + +def _make_default_propensity_model(): + return RandomForestClassifier( + n_estimators=50, + min_samples_leaf=5, + random_state=_DEFAULT_NUISANCE_RANDOM_STATE, + ) + + +def _make_default_survival_model(): + return RandomSurvivalForest( + n_estimators=50, + min_samples_leaf=5, + random_state=_DEFAULT_NUISANCE_RANDOM_STATE, + ) + + +def _resolve_propensity_model(model): + return _make_default_propensity_model() if model == 'auto' else model + + +def _resolve_survival_model(model): + return _make_default_survival_model() if model == 'auto' else model + + +class NuisanceResult: + """Container for fitted nuisance survival matrices. + + Attributes + ---------- + time_grid : ndarray (ns,) + Sorted time grid on which all matrices are evaluated. + G_a0, G_a1 : ndarray (n, ns) or None + Censoring survival P(C > t | a, X). + S_a0, S_a1 : ndarray (n, ns) or None + Overall event survival P(T > t | a, X). + Sj_a0, Sj_a1 : ndarray (n, ns) or None + Cause-j subdistribution survival. + Sjbar_a0, Sjbar_a1 : ndarray (n, ns) or None + Competing-cause subdistribution survival. + """ + + def __init__(self, time_grid, **matrices): + self.time_grid = time_grid + self.G_a0 = matrices.get('G_a0') + self.G_a1 = matrices.get('G_a1') + self.S_a0 = matrices.get('S_a0') + self.S_a1 = matrices.get('S_a1') + self.Sj_a0 = matrices.get('Sj_a0') + self.Sj_a1 = matrices.get('Sj_a1') + self.Sjbar_a0 = matrices.get('Sjbar_a0') + self.Sjbar_a1 = matrices.get('Sjbar_a1') + + +def _make_sksurv_y(time, event_bool): + """Build scikit-survival structured array with dtype [('event', bool), ('time', float)].""" + return np.array( + [(bool(e), float(t)) for e, t in zip(event_bool, time)], + dtype=[('event', bool), ('time', float)] + ) + + +def _eval_on_grid(model, X, time_grid): + """Evaluate a fitted scikit-survival model's survival function on a time grid. + + Parameters + ---------- + model : fitted sksurv estimator + Must implement ``predict_survival_function(X)``. + X : ndarray (m, d) + time_grid : ndarray (ns,) + + Returns + ------- + S_mat : ndarray (m, ns) + ``S_mat[i, k] = S(time_grid[k] | X[i])``, clamped to [1e-3, 1]. + """ + surv_fns = model.predict_survival_function(X) + m = len(surv_fns) + ns = len(time_grid) + S_mat = np.empty((m, ns)) + for i, fn in enumerate(surv_fns): + grid = np.asarray(time_grid, dtype=float) + x = np.asarray(fn.x, dtype=float) + y = np.asarray(fn(x), dtype=float) + S_mat[i] = np.interp(grid, x, y, left=1.0, right=y[-1]) + return np.clip(S_mat, 1e-3, 1.0) + + +def _fit_and_eval_per_arm(model_template, X, time, event_bool, T_arr, time_grid, X_pred=None): + """Clone, fit per arm, and evaluate on a held-out prediction sample. + + Parameters + ---------- + model_template : sksurv estimator (unfitted) + X : ndarray (n, d) — training covariates + time : ndarray (n,) — observed times + event_bool : ndarray (n,) bool — event indicator for this model type + T_arr : ndarray (n,) int — treatment assignment (0/1) + time_grid : ndarray (ns,) — evaluation grid + X_pred : ndarray (m, d) + Held-out prediction covariates. In-sample nuisance predictions are + intentionally unsupported in this project. + + Returns + ------- + mat_a0 : ndarray (m, ns) + mat_a1 : ndarray (m, ns) + """ + if X_pred is None: + raise ValueError("_fit_and_eval_per_arm is OOS-only and requires X_pred") + + results = [] + for arm in (0, 1): + mask = T_arr == arm + if mask.sum() < 2: + # Not enough data for this arm — return ones (no information) + results.append(np.ones((X_pred.shape[0], len(time_grid)))) + continue + if not np.any(event_bool[mask]): + # No observed events for this nuisance within the fold/arm slice + results.append(np.ones((X_pred.shape[0], len(time_grid)))) + continue + m = clone(model_template) + y_arm = _make_sksurv_y(time[mask], event_bool[mask]) + try: + m.fit(X[mask], y_arm) + results.append(_eval_on_grid(m, X_pred, time_grid)) + except (FloatingPointError, ValueError, np.linalg.LinAlgError) as exc: + warnings.warn( + "Nuisance survival model fit failed on a fold/arm slice; " + "falling back to a constant survival curve. " + f"Original error: {exc}", + RuntimeWarning, + stacklevel=2, + ) + results.append(np.ones((X_pred.shape[0], len(time_grid)))) + + return results[0], results[1] + + +def fit_nuisance_survival(time, event, T, X, *, + model_censoring='auto', + model_event='auto', + model_cause='auto', + model_competing='auto', + propensity_model='auto', + cause=1, + time_grid=None, + X_pred=None, + cv=2, + random_state=None): + """Cross-fit per-arm survival nuisance models and evaluate on a common time grid. + + This project uses out-of-sample nuisance predictions only. Accordingly, + ``fit_nuisance_survival`` is now a thin compatibility wrapper around + :func:`fit_nuisance_survival_crossfit` and returns OOF nuisance matrices + on the training sample. + + Only models that are provided (not None) are fitted. For example, IPCW + only needs ``model_censoring``; AIPCW needs both ``model_censoring`` and + ``model_event``; competing-risk transforms also need ``model_cause``. + + Parameters + ---------- + time : ndarray (n,) + Observed (possibly censored) event times. + event : ndarray (n,) + Event indicator. 0 = censored, 1 = event (survival) or cause codes + (competing risks: 1 = cause j, 2 = competing cause, etc.). + T : ndarray (n,) + Binary treatment indicator (0/1). + X : ndarray (n, d) + Covariates. + model_censoring : scikit-survival estimator or None + Template for censoring survival G(t|a,X) = P(C > t | a, X). + Must implement ``fit(X, y)`` and ``predict_survival_function(X)``. + model_event : scikit-survival estimator or None + Template for overall event survival S(t|a,X) = P(T > t | a, X). + model_cause : scikit-survival estimator or None + Template for cause-j subdistribution survival S_j(t|a,X). + model_competing : scikit-survival estimator or None + Template for competing-cause survival S_jbar(t|a,X). + cause : int, default 1 + Target cause code (for ``model_cause`` and ``model_competing``). + time_grid : ndarray (ns,) or None + Sorted time points for evaluation. ``None`` → ``sort(unique(time))``. + X_pred : ndarray (m, d) or None + Unsupported in the OOS-only project configuration. Predictions are + always returned for the training rows using held-out folds. + cv : int or splitter, default 2 + Cross-fitting specification used to generate OOF nuisance predictions. + random_state : int or None, default None + Random seed used when ``cv`` is an integer. + + Returns + ------- + result : NuisanceResult + Container with OOF nuisance matrices on the training sample (those not + requested are ``None``). + + Examples + -------- + >>> from sksurv.linear_model import CoxPHSurvivalAnalysis + >>> from econml.censor import fit_nuisance_survival, aipcw_cut_rmst + >>> result = fit_nuisance_survival( + ... time, event, T, X, + ... model_censoring=CoxPHSurvivalAnalysis(), + ... model_event=CoxPHSurvivalAnalysis(), + ... cv=2) + >>> Y_star = aipcw_cut_rmst( + ... T, time, event, tau=4.0, + ... result.G_a0, result.G_a1, + ... result.S_a0, result.S_a1, + ... time_grid=result.time_grid) + """ + if X_pred is not None: + raise ValueError( + "fit_nuisance_survival is OOS-only in this project and does not " + "support X_pred. Use the returned training-row OOF nuisances.") + + result = fit_nuisance_survival_crossfit( + time, event, T, X, + model_censoring=model_censoring, + model_event=model_event, + model_cause=model_cause, + model_competing=model_competing, + propensity_model=propensity_model, + cause=cause, + time_grid=time_grid, + cv=cv, + random_state=random_state, + ) + return CrossFitNuisanceResult( + result.time_grid, + ps=result.ps, + iptw=result.iptw, + naive=result.naive, + ow=result.ow, + ow_tilt_hat=result.ow_tilt_hat, + G_a0=result.G_a0, + G_a1=result.G_a1, + S_a0=result.S_a0, + S_a1=result.S_a1, + Sj_a0=result.Sj_a0, + Sj_a1=result.Sj_a1, + Sjbar_a0=result.Sjbar_a0, + Sjbar_a1=result.Sjbar_a1, + ) + + +class CrossFitNuisanceResult(NuisanceResult): + """Container for cross-fitted nuisance matrices and propensity-derived weights. + + Attributes + ---------- + ps : ndarray (n,) or None + Out-of-fold propensity scores P(T=1 | X). + iptw : ndarray (n,) or None + ATE balancing weights ``1 / (ps * a + (1 - ps) * (1 - a))``. + naive : ndarray (n,) or None + Constant tilt weights for the ATE target. + ow : ndarray (n,) or None + Overlap balancing weights ``ps`` for controls and ``1 - ps`` for treated. + ow_tilt_hat : ndarray (n,) or None + Overlap tilt weights ``ps * (1 - ps)``. + """ + + def __init__(self, time_grid, *, ps=None, iptw=None, naive=None, + ow=None, ow_tilt_hat=None, **matrices): + super().__init__(time_grid, **matrices) + self.ps = ps + self.iptw = iptw + self.naive = naive + self.ow = ow + self.ow_tilt_hat = ow_tilt_hat + + +def _build_crossfit_folds(X, T_arr, event, cv, random_state): + """Create folds for nuisance cross-fitting, favoring treatment/event stratification.""" + from sklearn.model_selection import KFold, StratifiedKFold + + if hasattr(cv, "split"): + return list(cv.split(X, T_arr)) + + n_splits = int(cv) + if n_splits < 2: + raise ValueError("cv must be at least 2 for cross-fitted nuisance estimation") + + event_arr = np.asarray(event).astype(int).ravel() + strata_joint = np.asarray([f"{t}_{e}" for t, e in zip(T_arr, event_arr)]) + _, joint_counts = np.unique(strata_joint, return_counts=True) + if joint_counts.size > 0 and np.min(joint_counts) >= n_splits: + splitter = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state) + return list(splitter.split(X, strata_joint)) + + _, treatment_counts = np.unique(T_arr, return_counts=True) + if treatment_counts.size > 0 and np.min(treatment_counts) >= n_splits: + splitter = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state) + return list(splitter.split(X, T_arr)) + + splitter = KFold(n_splits=n_splits, shuffle=True, random_state=random_state) + return list(splitter.split(X)) + + +def _fit_and_eval_propensity(propensity_model, X_train, T_train, X_test): + """Fit a propensity model on a training fold and predict on its held-out fold.""" + m = clone(propensity_model) + m.fit(X_train, T_train) + if hasattr(m, "predict_proba"): + ps = m.predict_proba(X_test)[:, 1] + else: + ps = m.predict(X_test) + return np.clip(np.asarray(ps, dtype=float).ravel(), _PROPENSITY_CLIP, 1 - _PROPENSITY_CLIP) + + +def _sanitize_survival_matrix(mat): + """Clamp fold-wise survival predictions to a finite probability range.""" + arr = np.asarray(mat, dtype=float) + arr = np.nan_to_num(arr, nan=1.0, posinf=1.0, neginf=1e-3) + return np.clip(arr, 1e-3, 1.0) + + +def fit_nuisance_survival_crossfit(time, event, T, X, *, + model_censoring='auto', + model_event='auto', + model_cause='auto', + model_competing='auto', + propensity_model='auto', + cause=1, + time_grid=None, + cv=3, + random_state=None): + """Cross-fit survival nuisance models and return out-of-fold predictions. + + This utility mirrors :func:`fit_nuisance_survival`, but each nuisance model + is trained on fold complements and evaluated only on held-out observations. + The resulting matrices are ready to plug into CUT and UIF transformations. + + Parameters + ---------- + time, event, T, X : see :func:`fit_nuisance_survival` + model_censoring, model_event, model_cause, model_competing : see + :func:`fit_nuisance_survival` + propensity_model : sklearn classifier or None, default None + If provided, produces out-of-fold propensity scores and the associated + ATE / overlap balancing weights used by UIF transforms. + cause : int, default 1 + Target cause code for competing-risk nuisances. + time_grid : ndarray (ns,) or None + Common evaluation grid. ``None`` uses ``sort(unique(time))``. + cv : int or splitter, default 3 + Cross-fitting specification. Integers use shuffled folds. + random_state : int or None, default None + Random seed used when ``cv`` is an integer. + + Returns + ------- + result : CrossFitNuisanceResult + Out-of-fold nuisance matrices, optional propensity scores, and weight + vectors (`iptw`, `naive`, `ow`, `ow_tilt_hat`). + """ + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + T_arr = np.asarray(T, dtype=int).ravel() + X = np.asarray(X, dtype=float) + + if time_grid is None: + time_grid = np.sort(np.unique(time)) + + model_censoring = _resolve_survival_model(model_censoring) + model_event = _resolve_survival_model(model_event) + model_cause = _resolve_survival_model(model_cause) + model_competing = _resolve_survival_model(model_competing) + propensity_model = _resolve_propensity_model(propensity_model) + + n = len(time) + ns = len(time_grid) + + matrices = {} + for name, model in ( + ("G_a0", model_censoring), + ("G_a1", model_censoring), + ("S_a0", model_event), + ("S_a1", model_event), + ("Sj_a0", model_cause), + ("Sj_a1", model_cause), + ("Sjbar_a0", model_competing), + ("Sjbar_a1", model_competing), + ): + matrices[name] = np.full((n, ns), np.nan) if model is not None else None + + ps = np.full(n, np.nan) if propensity_model is not None else None + folds = _build_crossfit_folds(X, T_arr, event, cv, random_state) + + for train_idx, test_idx in folds: + X_train = X[train_idx] + X_test = X[test_idx] + time_train = time[train_idx] + event_train = event[train_idx] + T_train = T_arr[train_idx] + + if model_censoring is not None: + g0, g1 = _fit_and_eval_per_arm( + model_censoring, X_train, time_train, event_train == 0, + T_train, time_grid, X_pred=X_test) + matrices["G_a0"][test_idx] = _sanitize_survival_matrix(g0) + matrices["G_a1"][test_idx] = _sanitize_survival_matrix(g1) + + if model_event is not None: + s0, s1 = _fit_and_eval_per_arm( + model_event, X_train, time_train, event_train != 0, + T_train, time_grid, X_pred=X_test) + matrices["S_a0"][test_idx] = _sanitize_survival_matrix(s0) + matrices["S_a1"][test_idx] = _sanitize_survival_matrix(s1) + + if model_cause is not None: + sj0, sj1 = _fit_and_eval_per_arm( + model_cause, X_train, time_train, event_train == cause, + T_train, time_grid, X_pred=X_test) + matrices["Sj_a0"][test_idx] = _sanitize_survival_matrix(sj0) + matrices["Sj_a1"][test_idx] = _sanitize_survival_matrix(sj1) + + if model_competing is not None: + sjbar0, sjbar1 = _fit_and_eval_per_arm( + model_competing, X_train, time_train, event_train > cause, + T_train, time_grid, X_pred=X_test) + matrices["Sjbar_a0"][test_idx] = _sanitize_survival_matrix(sjbar0) + matrices["Sjbar_a1"][test_idx] = _sanitize_survival_matrix(sjbar1) + + if propensity_model is not None: + ps[test_idx] = _fit_and_eval_propensity(propensity_model, X_train, T_train, X_test) + + for name, value in matrices.items(): + if value is not None and np.isnan(value).any(): + raise RuntimeError(f"Cross-fitted nuisance matrix {name} contains unfilled entries") + + iptw = None + naive = None + ow = None + ow_tilt_hat = None + if ps is not None: + if np.isnan(ps).any(): + raise RuntimeError("Cross-fitted propensity scores contain unfilled entries") + iptw = 1.0 / (ps * T_arr + (1.0 - ps) * (1.0 - T_arr)) + naive = np.ones_like(ps) + ow = np.where(T_arr == 1, 1.0 - ps, ps) + ow_tilt_hat = ps * (1.0 - ps) + + return CrossFitNuisanceResult( + time_grid, + ps=ps, + iptw=iptw, + naive=naive, + ow=ow, + ow_tilt_hat=ow_tilt_hat, + **matrices, + ) + + +def fit_nuisance_competing_crossfit(time, event, T, X, *, + model_censoring='auto', + model_event='auto', + model_cause='auto', + model_competing='auto', + propensity_model='auto', + cause=1, + time_grid=None, + cv=3, + random_state=None): + """Cross-fit nuisance functions for competing-risk CUT and UIF transforms.""" + return fit_nuisance_survival_crossfit( + time, event, T, X, + model_censoring=model_censoring, + model_event=model_event, + model_cause=model_cause, + model_competing=model_competing, + propensity_model=propensity_model, + cause=cause, + time_grid=time_grid, + cv=cv, + random_state=random_state, + ) diff --git a/econml/censor/_transformations.py b/econml/censor/_transformations.py new file mode 100644 index 000000000..50b9f6039 --- /dev/null +++ b/econml/censor/_transformations.py @@ -0,0 +1,1218 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +""" +Censoring Unbiased Transformations (CUT) and Influence Function (UIF) transforms. + +These functions transform censored survival/competing-risk data into per-subject +pseudo-outcomes that can be fed into any standard HTE learner. + +**RMST transforms** (from ``survival_cut.R``): + ipcw_cut_rmst, bj_cut_rmst, aipcw_cut_rmst, uif_diff_rmst + +**RMTL transforms** (from ``competing_cut.R``): + ipcw_cut_rmtlj, bj_cut_rmtlj, aipcw_cut_rmtlj, + aipcw_cut_rmtlj_sep_direct_astar1, aipcw_cut_rmtlj_sep_indirect_astar1, + uif_diff_rmtlj, uif_diff_rmtlj_sep_direct_astar1, uif_diff_rmtlj_sep_indirect_astar1 + +All survival/censoring matrices are shape ``(n, ns)`` where ``n`` is the number +of subjects and ``ns`` is the number of time-grid points. Functions return +per-subject pseudo-outcomes of shape ``(n,)``. +""" + +import numpy as np + + +# --------------------------------------------------------------------------- +# Shared helpers +# --------------------------------------------------------------------------- + +def _setup_time_grid(time, tau, time_grid=None, admin_cens=None): + """Build time grid, interval widths, and tau interpolation index. + + Parameters + ---------- + time : ndarray (n,) + Observed event/censoring times. + tau : float + Time horizon. + time_grid : ndarray, float, or None + - ``ndarray``: pre-built sorted time grid. + - ``float``: frequency step — builds ``arange(freq, admin_cens, freq)``. + - ``None``: built from ``sort(unique(time))``. + admin_cens : float or None + Administrative censoring time (required when *time_grid* is a float). + + Returns + ------- + s : ndarray (ns,) + Sorted time-grid points. + ds : ndarray (ns,) + Interval widths ``diff([0, s])``. + ind : int + Last index where ``s[ind] < tau``. + """ + if time_grid is not None: + if isinstance(time_grid, np.ndarray): + s = time_grid + elif np.isscalar(time_grid): + freq_time = float(time_grid) + if admin_cens is None: + raise ValueError("admin_cens is required when time_grid is a frequency") + s = np.arange(freq_time, admin_cens + freq_time * 0.5, freq_time) + else: + s = np.asarray(time_grid, dtype=float) + else: + s = np.sort(np.unique(time)) + ds = np.diff(np.concatenate([[0.0], s])) + ind = int(np.max(np.where(s < tau)[0])) + return s, ds, ind + + +def _clamp_survival(S, min_val=1e-3): + """Clamp survival matrix to ``[min_val, 1]`` and forward-fill NaN along time axis. + + Mirrors R's ``t(na.locf(t(ifelse(S < 1e-3, 1e-3, S))))``. + """ + S = np.clip(S, min_val, 1.0) + # Forward-fill NaN along rows (time axis = axis 1) + mask = np.isnan(S) + if mask.any(): + for j in range(1, S.shape[1]): + bad = np.isnan(S[:, j]) + S[bad, j] = S[bad, j - 1] + return S + + +def _clamp_product_denom(*terms, min_val=1e-3): + """Form a denominator as a product of nuisance arrays and floor it at ``min_val``. + + This is stricter than clipping each factor separately and avoids tiny formed + products such as ``S * G`` or ``Sjbar * S * G`` destabilizing the transforms. + """ + den = np.ones_like(np.asarray(terms[0], dtype=float), dtype=float) + for term in terms: + den = den * np.asarray(term, dtype=float) + return np.maximum(den, min_val) + + +def _incremental_hazard(S): + """Incremental cumulative hazard: ``diff([0, -log(S)])`` per row. + + Mirrors R's ``t(apply(cbind(0, -log(S)), 1, diff))``. + + Returns ndarray (n, ns). + """ + n, ns = S.shape + neg_log_S = -np.log(S) + return np.diff(np.hstack([np.zeros((n, 1)), neg_log_S]), axis=1) + + +def _compute_cif_matrix(S_overall, dH_cause): + """Product-limit CIF from overall survival and cause-j hazard increments. + + ``Fj[k] = cumsum_{l=0}^{k} S_lag[l] * dH_j[l]`` + + where ``S_lag = hstack([1, S_overall[:, :-1]])``. + + Mirrors R's ``t(apply(cbind(1, S[,1:(ns-1)]) * Fj.dHazard, 1, cumsum))``. + + Returns ndarray (n, ns). + """ + n, ns = S_overall.shape + S_lag = np.hstack([np.ones((n, 1)), S_overall[:, :ns - 1]]) + return np.cumsum(S_lag * dH_cause, axis=1) + + +def _cumulative_integral(values, ds): + """Row-wise cumulative sum of ``values * ds``. + + Mirrors R's ``t(apply(S * ds_matrix, 1, cumsum))``. + + Returns ndarray (n, ns). + """ + return np.cumsum(values * ds[np.newaxis, :], axis=1) + + +def _interpolate_to_tau(matrix, s, tau, ind): + """Linearly interpolate an ``(n, ns)`` matrix between columns *ind* and *ind+1* + to the exact time *tau*. + + Mirrors R's ``M[, ind] + (tau - s[ind]) * (M[, ind+1] - M[, ind]) / (s[ind+1] - s[ind])``. + + Returns ndarray (n,). + """ + frac = (tau - s[ind]) / (s[ind + 1] - s[ind]) + return matrix[:, ind] + frac * (matrix[:, ind + 1] - matrix[:, ind]) + + +def _build_indicators(time, event, s, cause=None): + """Build counting-process indicator matrices. + + Parameters + ---------- + time : ndarray (n,) + event : ndarray (n,) integer event codes (0 = censored) + s : ndarray (ns,) time grid + cause : int or None + If given, also build cause-j and competing-cause indicators. + + Returns + ------- + dict with keys: + ``Yt`` (n, ns) at-risk indicator ``time >= s[k]`` + ``dNct`` (n, ns) censoring jump ``time ≈ s[k] and event == 0`` + ``dNt`` (n, ns) any-event jump ``time ≈ s[k] and event != 0`` + ``dNjt`` (n, ns) cause-j jump (only if *cause* given) + ``dNjbart`` (n, ns) competing-cause jump (only if *cause* given) + """ + t_col = time[:, np.newaxis] # (n, 1) + s_row = s[np.newaxis, :] # (1, ns) + + Yt = (t_col >= s_row).astype(float) + at_time = np.isclose(t_col, s_row, atol=1e-10, rtol=0) # (n, ns) bool + + dNct = at_time * (event[:, np.newaxis] == 0).astype(float) + dNt = at_time * (event[:, np.newaxis] != 0).astype(float) + + out = dict(Yt=Yt, dNct=dNct, dNt=dNt) + if cause is not None: + out['dNjt'] = at_time * (event[:, np.newaxis] == cause).astype(float) + out['dNjbart'] = at_time * (event[:, np.newaxis] > cause).astype(float) + return out + + +def _ipcw_weight_matrix(time, event, G, s): + r"""IPCW debiasing weight matrix :math:`\Delta / G`. + + For subject *i* at grid point *k*: + + - If ``time_i > s[k]``: weight = ``1 / G(s[k])`` (still at risk) + - If ``time_i <= s[k]`` and ``event_i != 0``: weight = ``event_i / G(time_i)`` + - If ``time_i <= s[k]`` and ``event_i == 0``: weight = 0 + + Mirrors R lines 17-18 of ``survival_cut.R``. + + Returns ndarray (n, ns). + """ + n = len(time) + ns = len(s) + t_col = time[:, np.newaxis] + s_row = s[np.newaxis, :] + + # Part 1: (time > s[k]) / G + still_at_risk = (t_col > s_row).astype(float) / G + + # Part 2: event / G(time_i) * (time <= s[k]) + # G(time_i) = G evaluated at observed time → rowSums(G * indicator_at_time) + at_time = np.isclose(t_col, s_row, atol=1e-10, rtol=0) + G_at_obs = np.sum(G * at_time, axis=1) # (n,) + G_at_obs = np.maximum(G_at_obs, 1e-3) # safety clamp + event_weight = (event / G_at_obs)[:, np.newaxis] # (n, 1) + after_event = (t_col <= s_row).astype(float) + + return still_at_risk + event_weight * after_event + + +def _extract_at_time(matrix, time, s): + """Extract ``matrix[i, k]`` where ``s[k] ≈ time[i]`` for each row. + + Returns ndarray (n,). + """ + at_time = np.isclose(time[:, np.newaxis], s[np.newaxis, :], atol=1e-10, rtol=0) + return np.sum(matrix * at_time, axis=1) + + +# =========================================================================== +# RMST transformations (from survival_cut.R) +# =========================================================================== + +def ipcw_cut_rmst(a, time, event, tau, G_a0, G_a1, + time_grid=None, admin_cens=None): + """IPCW censoring unbiased transformation for RMST. + + Singly robust — requires correct censoring model G. + + Parameters + ---------- + a : ndarray (n,) + Binary treatment indicator (0/1). + time : ndarray (n,) + Observed (possibly censored) event times. + event : ndarray (n,) + Event indicator (1 = event, 0 = censored). + tau : float + RMST truncation horizon. + G_a0, G_a1 : ndarray (n, ns) + Censoring survival matrices for arm 0 and arm 1. + time_grid : ndarray (ns,) or None + Sorted time grid. ``None`` → built from ``sort(unique(time))``. + admin_cens : float or None + Administrative censoring time (for fixed-frequency grid). + + Returns + ------- + pseudo_y : ndarray (n,) + Per-subject pseudo-outcome. + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=float).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + + # IPCW weight × min(time, s[k]) + s_row = s[np.newaxis, :] # (1, ns) + t_col = time[:, np.newaxis] # (n, 1) + min_time_s = np.minimum(t_col, s_row) # (n, ns) + + DG_a0 = _ipcw_weight_matrix(time, event, G_a0, s) + DG_a1 = _ipcw_weight_matrix(time, event, G_a1, s) + + cut_a0 = DG_a0 * min_time_s + cut_a1 = DG_a1 * min_time_s + + cut_a0 = _interpolate_to_tau(cut_a0, s, tau, ind) + cut_a1 = _interpolate_to_tau(cut_a1, s, tau, ind) + + return cut_a0 * (1 - a) + cut_a1 * a + + +def bj_cut_rmst(a, time, event, tau, S_a0, S_a1, + time_grid=None, admin_cens=None): + """Buckley-James censoring unbiased transformation for RMST. + + Singly robust — requires correct event survival model S. + + Parameters + ---------- + a, time, event, tau : see :func:`ipcw_cut_rmst`. + S_a0, S_a1 : ndarray (n, ns) + Event survival matrices for arm 0 and arm 1. + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=float).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + + min_time_tau = np.minimum(time, tau) + + results = [] + for S in (S_a0, S_a1): + # S at tau + S_tau = _interpolate_to_tau(S, s, tau, ind) + + # S at observed time + S_Ttilde = _extract_at_time(S, time, s) + + # S at min(tau, time) + S_min = np.where(time > tau, S_tau, S_Ttilde) + + # RMST cumulative integral + RMST = _cumulative_integral(S, ds) + RMST_tau = _interpolate_to_tau(RMST, s, tau, ind) + RMST_Ttilde = _extract_at_time(RMST, time, s) + RMST_min = np.where(time > tau, RMST_tau, RMST_Ttilde) + + # BJ formula: min(time, tau) + (1 - event*(time<=tau) - (time>tau)) * (RMST_tau - RMST_min) / S_min + indicator = 1.0 - event * (time <= tau).astype(float) - (time > tau).astype(float) + cut = min_time_tau + indicator * (RMST_tau - RMST_min) / np.maximum(S_min, 1e-3) + results.append(cut) + + return results[0] * (1 - a) + results[1] * a + + +def aipcw_cut_rmst(a, time, event, tau, G_a0, G_a1, S_a0, S_a1, + time_grid=None, admin_cens=None): + """AIPCW (doubly robust) censoring unbiased transformation for RMST. + + Doubly robust — requires correct censoring model G **or** correct event model S. + + Parameters + ---------- + a, time, event, tau : see :func:`ipcw_cut_rmst`. + G_a0, G_a1 : ndarray (n, ns) + Censoring survival matrices. + S_a0, S_a1 : ndarray (n, ns) + Event survival matrices. + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=float).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + + ind_dict = _build_indicators(time, event, s) + Yt = ind_dict['Yt'] + dNct = ind_dict['dNct'] + + s_row = s[np.newaxis, :] + t_col = time[:, np.newaxis] + min_time_s = np.minimum(t_col, s_row) + + results = [] + for S, G in ((S_a0, G_a0), (S_a1, G_a1)): + RMST = _cumulative_integral(S, ds) + G_dH = _incremental_hazard(G) + SG = _clamp_product_denom(S, G) + + DG = _ipcw_weight_matrix(time, event, G, s) + + # term1: IPCW + term1 = DG * min_time_s + + # term3: cumsum of s * (dNc - Y*dH_G) / G + martingale = (dNct - Yt * G_dH) / G + term3 = np.cumsum(s_row * martingale, axis=1) + + # term4: RMST * cumsum((dNc - Y*dH_G) / (S*G)) + term4 = RMST * np.cumsum((dNct - Yt * G_dH) / SG, axis=1) + + # term5: cumsum(RMST * (dNc - Y*dH_G) / (S*G)) + term5 = np.cumsum(RMST * (dNct - Yt * G_dH) / SG, axis=1) + + cut = term1 + term3 + term4 - term5 + results.append(_interpolate_to_tau(cut, s, tau, ind)) + + return results[0] * (1 - a) + results[1] * a + + +def uif_diff_rmst(a, time, event, tau, bw, tilt, G_a0, G_a1, S_a0, S_a1, + time_grid=None, admin_cens=None): + """Uncentered influence function transformation for RMST difference. + + Returns treatment-effect pseudo-outcome directly (``uif_a1 - uif_a0``). + + Parameters + ---------- + a, time, event, tau : see :func:`ipcw_cut_rmst`. + bw : ndarray (n,) + Balancing weights. + tilt : ndarray (n,) + Tilting weights. + G_a0, G_a1, S_a0, S_a1 : ndarray (n, ns) + Censoring and event survival matrices. + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + UIF difference pseudo-outcome per subject. + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=float).ravel() + bw = np.asarray(bw, dtype=float).ravel() + tilt = np.asarray(tilt, dtype=float).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + + ind_dict = _build_indicators(time, event, s) + Yt = ind_dict['Yt'] + dNct = ind_dict['dNct'] + + s_row = s[np.newaxis, :] + t_col = time[:, np.newaxis] + min_time_s = np.minimum(t_col, s_row) + + uif_arms = [] + for arm, S, G in ((0, S_a0, G_a0), (1, S_a1, G_a1)): + RMST = _cumulative_integral(S, ds) + G_dH = _incremental_hazard(G) + SG = _clamp_product_denom(S, G) + DG = _ipcw_weight_matrix(time, event, G, s) + + bw_arm = bw * (a == arm).astype(float) + bw_mat = bw_arm[:, np.newaxis] # (n, 1) + tilt_mat = tilt[:, np.newaxis] # (n, 1) + + martingale = (dNct - Yt * G_dH) / G + + term1 = bw_mat * DG * min_time_s + term2 = tilt_mat * RMST + term3 = bw_mat * np.cumsum(s_row * martingale, axis=1) + term4 = bw_mat * RMST * (np.cumsum((dNct - Yt * G_dH) / SG, axis=1) - 1.0) + term5 = bw_mat * np.cumsum(RMST * (dNct - Yt * G_dH) / SG, axis=1) + + mean_tilt = np.mean(tilt) + mean_bw_arm = np.mean(bw_arm) + # Avoid division by zero + mean_tilt = max(mean_tilt, 1e-10) + mean_bw_arm = max(mean_bw_arm, 1e-10) + + uif = (1.0 / mean_tilt) * term2 + (1.0 / mean_bw_arm) * (term1 + term3 + term4 - term5) + uif_arms.append(_interpolate_to_tau(uif, s, tau, ind)) + + return uif_arms[1] - uif_arms[0] + + +# =========================================================================== +# RMTL transformations (from competing_cut.R) +# =========================================================================== + +def ipcw_cut_rmtlj(a, time, event, tau, G_a0, G_a1, cause=1, + time_grid=None, admin_cens=None): + """IPCW censoring unbiased transformation for RMTL (cause j). + + Singly robust — requires correct censoring model G. + + Parameters + ---------- + a, time, event, tau : see :func:`ipcw_cut_rmst`. + *event* uses codes 0 = censored, 1 = cause 1, 2 = cause 2, etc. + G_a0, G_a1 : ndarray (n, ns) + Censoring survival matrices. + cause : int, default 1 + Target cause. + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + + # Observed RMTL contribution: (s[k] - min(time, s[k])) * (event == cause) + s_row = s[np.newaxis, :] + t_col = time[:, np.newaxis] + is_cause = (event == cause).astype(float)[:, np.newaxis] + RMTLj_obs = (s_row - np.minimum(t_col, s_row)) * is_cause # (n, ns) + + results = [] + for G in (G_a0, G_a1): + # G at observed time + G_at_obs = _extract_at_time(G, time, s) + G_at_obs = np.maximum(G_at_obs, 1e-3) + + term1 = RMTLj_obs / G_at_obs[:, np.newaxis] + results.append(_interpolate_to_tau(term1, s, tau, ind)) + + return results[0] * (1 - a) + results[1] * a + + +def bj_cut_rmtlj(a, time, event, tau, S_a0, S_a1, Sj_a0, Sj_a1, cause=1, + time_grid=None, admin_cens=None): + """Buckley-James censoring unbiased transformation for RMTL (cause j). + + Singly robust — requires correct event survival model S and cause-j model Sj. + + Parameters + ---------- + a, time, event, tau, cause : see :func:`ipcw_cut_rmtlj`. + S_a0, S_a1 : ndarray (n, ns) + Overall event survival matrices. + Sj_a0, Sj_a1 : ndarray (n, ns) + Cause-j specific survival matrices. + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + Sj_a0 = _clamp_survival(np.array(Sj_a0, dtype=float, copy=True)) + Sj_a1 = _clamp_survival(np.array(Sj_a1, dtype=float, copy=True)) + + min_time_tau = np.minimum(time, tau) + is_cause = (event == cause).astype(float) + is_any_event = (event != 0).astype(float) + + results = [] + for S, Sj in ((S_a0, Sj_a0), (S_a1, Sj_a1)): + # Overall survival at tau and observed time + S_tau = _interpolate_to_tau(S, s, tau, ind) + S_Ttilde = _extract_at_time(S, time, s) + S_min = np.where(time > tau, S_tau, S_Ttilde) + + # CIF Fj via product-limit + Fj_dH = _incremental_hazard(Sj) + Fj = _compute_cif_matrix(S, Fj_dH) + + Fj_tau = _interpolate_to_tau(Fj, s, tau, ind) + Fj_Ttilde = _extract_at_time(Fj, time, s) + Fj_min = np.where(time > tau, Fj_tau, Fj_Ttilde) + + # RMTL = cumulative integral of Fj + RMTLj = _cumulative_integral(Fj, ds) + RMTLj_tau = _interpolate_to_tau(RMTLj, s, tau, ind) + RMTLj_Ttilde = _extract_at_time(RMTLj, time, s) + RMTLj_min = np.where(time > tau, RMTLj_tau, RMTLj_Ttilde) + + # BJ formula for RMTL + indicator = 1.0 - is_any_event * (time <= tau).astype(float) - (time > tau).astype(float) + cut = ((tau - min_time_tau) * is_cause + + indicator * (RMTLj_tau - RMTLj_min - Fj_min * (tau - min_time_tau)) + / np.maximum(S_min, 1e-3)) + results.append(cut) + + return results[0] * (1 - a) + results[1] * a + + +def aipcw_cut_rmtlj(a, time, event, tau, G_a0, G_a1, S_a0, S_a1, + Sj_a0, Sj_a1, cause=1, time_grid=None, admin_cens=None): + """AIPCW (doubly robust) censoring unbiased transformation for RMTL. + + Parameters + ---------- + a, time, event, tau, cause : see :func:`ipcw_cut_rmtlj`. + G_a0, G_a1, S_a0, S_a1, Sj_a0, Sj_a1 : ndarray (n, ns) + Censoring, overall event, and cause-j survival matrices. + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + Sj_a0 = _clamp_survival(np.array(Sj_a0, dtype=float, copy=True)) + Sj_a1 = _clamp_survival(np.array(Sj_a1, dtype=float, copy=True)) + + ind_dict = _build_indicators(time, event, s, cause=cause) + Yt = ind_dict['Yt'] + dNct = ind_dict['dNct'] + + s_row = s[np.newaxis, :] + t_col = time[:, np.newaxis] + is_cause = (event == cause).astype(float)[:, np.newaxis] + RMTLj_obs = (s_row - np.minimum(t_col, s_row)) * is_cause + + results = [] + for S, G, Sj in ((S_a0, G_a0, Sj_a0), (S_a1, G_a1, Sj_a1)): + G_dH = _incremental_hazard(G) + Fj_dH = _incremental_hazard(Sj) + Fj = _compute_cif_matrix(S, Fj_dH) + RMTLj = _cumulative_integral(Fj, ds) + SG = _clamp_product_denom(S, G) + + martingale_c = (dNct - Yt * G_dH) / G # censoring martingale / G + + # G at observed time for IPCW + G_at_obs = _extract_at_time(G, time, s) + G_at_obs = np.maximum(G_at_obs, 1e-3) + + # term1: IPCW + term1 = RMTLj_obs / G_at_obs[:, np.newaxis] + + # term3: cumsum(s * Fj * martingale_c / S) - s * cumsum(Fj * martingale_c / S) + Fj_mc_over_S = Fj * martingale_c / S + term3 = (np.cumsum(s_row * Fj_mc_over_S, axis=1) + - s_row * np.cumsum(Fj_mc_over_S, axis=1)) + + # term4: RMTLj * cumsum(martingale_c / (S * G) ... wait, we already divided by G) + # R: RMTLj * cumsum((dNc - Y*dH_G) / (S*G)) + mc_over_SG = (dNct - Yt * G_dH) / SG + term4 = RMTLj * np.cumsum(mc_over_SG, axis=1) + + # term5: cumsum(RMTLj * (dNc - Y*dH_G) / (S*G)) + term5 = np.cumsum(RMTLj * mc_over_SG, axis=1) + + cut = term1 + term3 + term4 - term5 + results.append(_interpolate_to_tau(cut, s, tau, ind)) + + return results[0] * (1 - a) + results[1] * a + + +def aipcw_cut_rmtlj_sep_direct_astar1(a, time, event, tau, G_a0, G_a1, + S_a0, S_a1, Sj_a0, Sj_a1, + Sjbar_a0, Sjbar_a1, cause=1, + time_grid=None, admin_cens=None): + """AIPCW CUT for separable direct effect of RMTL (a* = 1). + + Parameters + ---------- + a, time, event, tau, cause : see :func:`ipcw_cut_rmtlj`. + G_a0, G_a1, S_a0, S_a1, Sj_a0, Sj_a1 : ndarray (n, ns) + Sjbar_a0, Sjbar_a1 : ndarray (n, ns) + Competing-cause survival matrices. + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + Sj_a0 = _clamp_survival(np.array(Sj_a0, dtype=float, copy=True)) + Sj_a1 = _clamp_survival(np.array(Sj_a1, dtype=float, copy=True)) + Sjbar_a0 = _clamp_survival(np.array(Sjbar_a0, dtype=float, copy=True)) + Sjbar_a1 = _clamp_survival(np.array(Sjbar_a1, dtype=float, copy=True)) + + a_col = a[:, np.newaxis] + S_a = S_a0 * (1 - a_col) + S_a1 * a_col + G_a = G_a0 * (1 - a_col) + G_a1 * a_col + Sj_a = Sj_a0 * (1 - a_col) + Sj_a1 * a_col + Sjbar_a = Sjbar_a0 * (1 - a_col) + Sjbar_a1 * a_col + + ind_dict = _build_indicators(time, event, s, cause=cause) + Yt = ind_dict['Yt'] + dNt = ind_dict['dNt'] + dNjt = ind_dict['dNjt'] + dNjbart = ind_dict['dNjbart'] + + s_row = s[np.newaxis, :] + + # Hazard increments + Fj_dH_a0 = _incremental_hazard(Sj_a0) + Fj_dH_a1 = _incremental_hazard(Sj_a1) + S_dH_a0 = _incremental_hazard(S_a0) + S_dH_a1 = _incremental_hazard(S_a1) + Fjbar_dH_a0 = _incremental_hazard(Sjbar_a0) + Fjbar_dH_a1 = _incremental_hazard(Sjbar_a1) + + # Martingales + dMjt_a0 = dNjt - Yt * Fj_dH_a0 + dMjt_a1 = dNjt - Yt * Fj_dH_a1 + dMjt_a = dMjt_a0 * (1 - a_col) + dMjt_a1 * a_col + + dMt_a0 = dNt - Yt * S_dH_a0 + dMt_a1 = dNt - Yt * S_dH_a1 + dMt_a = dMt_a0 * (1 - a_col) + dMt_a1 * a_col + + dMjbart_a0 = dNjbart - Yt * Fjbar_dH_a0 + dMjbart_a1 = dNjbart - Yt * Fjbar_dH_a1 + dMjbart_a = dMjbart_a0 * (1 - a_col) + dMjbart_a1 * a_col + + # CIF and RMTL + Fj_a0 = _compute_cif_matrix(S_a0, Fj_dH_a0) + Fj_a1 = _compute_cif_matrix(S_a1, Fj_dH_a1) + Fj_a = Fj_a0 * (1 - a_col) + Fj_a1 * a_col + + # Cross-world CIF: Fj(a_j=0, a_jbar=1) + S_cross = np.clip(Sj_a0 * Sjbar_a1, 1e-3, 1.0) + Fj_cross = _compute_cif_matrix(S_cross, Fj_dH_a0) + + RMTLj_a0 = _cumulative_integral(Fj_a0, ds) + RMTLj_a1 = _cumulative_integral(Fj_a1, ds) + RMTLj_a = RMTLj_a0 * (1 - a_col) + RMTLj_a1 * a_col + RMTLj_cross = _cumulative_integral(Fj_cross, ds) + + # 6 terms + # term1: plug-in + term1 = RMTLj_cross * (1 - a_col) + RMTLj_a1 * a_col + + # term2 + w2 = Sjbar_a1 * dMjt_a / _clamp_product_denom(Sjbar_a, G_a) + term2 = np.cumsum(s_row * w2, axis=1) - s_row * np.cumsum(w2, axis=1) + + # term3 + w3 = Sjbar_a1 * dMt_a / _clamp_product_denom(Sjbar_a, S_a, G_a) + term3 = -(RMTLj_a * np.cumsum(w3, axis=1) - np.cumsum(RMTLj_a * w3, axis=1)) + + # term4 + w4 = Sjbar_a1 * Fj_a * dMt_a / _clamp_product_denom(Sjbar_a, S_a, G_a) + term4 = np.cumsum(s_row * w4, axis=1) - s_row * np.cumsum(w4, axis=1) + + # term5: only for a==0 + is_a0 = (a == 0).astype(float)[:, np.newaxis] + w5 = dMjbart_a / _clamp_product_denom(S_a, G_a) + term5 = is_a0 * (RMTLj_cross * np.cumsum(w5, axis=1) - np.cumsum(RMTLj_cross * w5, axis=1)) + + # term6: only for a==0 + w6 = Fj_cross * dMjbart_a / _clamp_product_denom(S_a, G_a) + term6 = -is_a0 * (np.cumsum(s_row * w6, axis=1) - s_row * np.cumsum(w6, axis=1)) + + cut = term1 + term2 + term3 + term4 + term5 + term6 + return _interpolate_to_tau(cut, s, tau, ind) + + +def aipcw_cut_rmtlj_sep_indirect_astar1(a, time, event, tau, G_a0, G_a1, + S_a0, S_a1, Sj_a0, Sj_a1, + Sjbar_a0, Sjbar_a1, cause=1, + time_grid=None, admin_cens=None): + """AIPCW CUT for separable indirect effect of RMTL (a* = 1). + + Parameters + ---------- + Same as :func:`aipcw_cut_rmtlj_sep_direct_astar1`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + Sj_a0 = _clamp_survival(np.array(Sj_a0, dtype=float, copy=True)) + Sj_a1 = _clamp_survival(np.array(Sj_a1, dtype=float, copy=True)) + Sjbar_a0 = _clamp_survival(np.array(Sjbar_a0, dtype=float, copy=True)) + Sjbar_a1 = _clamp_survival(np.array(Sjbar_a1, dtype=float, copy=True)) + + a_col = a[:, np.newaxis] + S_a = S_a0 * (1 - a_col) + S_a1 * a_col + G_a = G_a0 * (1 - a_col) + G_a1 * a_col + Sj_a = Sj_a0 * (1 - a_col) + Sj_a1 * a_col + Sjbar_a = Sjbar_a0 * (1 - a_col) + Sjbar_a1 * a_col + + ind_dict = _build_indicators(time, event, s, cause=cause) + Yt = ind_dict['Yt'] + dNt = ind_dict['dNt'] + dNjt = ind_dict['dNjt'] + dNjbart = ind_dict['dNjbart'] + + s_row = s[np.newaxis, :] + + Fj_dH_a0 = _incremental_hazard(Sj_a0) + Fj_dH_a1 = _incremental_hazard(Sj_a1) + S_dH_a0 = _incremental_hazard(S_a0) + S_dH_a1 = _incremental_hazard(S_a1) + Fjbar_dH_a0 = _incremental_hazard(Sjbar_a0) + Fjbar_dH_a1 = _incremental_hazard(Sjbar_a1) + + dMjt_a0 = dNjt - Yt * Fj_dH_a0 + dMjt_a1 = dNjt - Yt * Fj_dH_a1 + dMjt_a = dMjt_a0 * (1 - a_col) + dMjt_a1 * a_col + + dMt_a0 = dNt - Yt * S_dH_a0 + dMt_a1 = dNt - Yt * S_dH_a1 + dMt_a = dMt_a0 * (1 - a_col) + dMt_a1 * a_col + + dMjbart_a0 = dNjbart - Yt * Fjbar_dH_a0 + dMjbart_a1 = dNjbart - Yt * Fjbar_dH_a1 + dMjbart_a = dMjbart_a0 * (1 - a_col) + dMjbart_a1 * a_col + + Fj_a0 = _compute_cif_matrix(S_a0, Fj_dH_a0) + Fj_a1 = _compute_cif_matrix(S_a1, Fj_dH_a1) + Fj_a = Fj_a0 * (1 - a_col) + Fj_a1 * a_col + + S_cross = np.clip(Sj_a0 * Sjbar_a1, 1e-3, 1.0) + Fj_cross = _compute_cif_matrix(S_cross, Fj_dH_a0) + + RMTLj_a0 = _cumulative_integral(Fj_a0, ds) + RMTLj_a1 = _cumulative_integral(Fj_a1, ds) + RMTLj_a = RMTLj_a0 * (1 - a_col) + RMTLj_a1 * a_col + RMTLj_cross = _cumulative_integral(Fj_cross, ds) + + # 6 terms — indirect effect + # term1: different from direct + term1 = RMTLj_a0 * (1 - a_col) + RMTLj_cross * a_col + + # term2: weighted by (1 - Sjbar_a1 / Sjbar_a) + ratio = 1.0 - Sjbar_a1 / Sjbar_a + w2 = dMjt_a / G_a * ratio + term2 = np.cumsum(s_row * w2, axis=1) - s_row * np.cumsum(w2, axis=1) + + # term3 + w3 = dMt_a / _clamp_product_denom(S_a, G_a) * ratio + term3 = -(RMTLj_a * np.cumsum(w3, axis=1) - np.cumsum(RMTLj_a * w3, axis=1)) + + # term4 + w4 = Fj_a * dMt_a / _clamp_product_denom(S_a, G_a) * ratio + term4 = np.cumsum(s_row * w4, axis=1) - s_row * np.cumsum(w4, axis=1) + + # term5: only for a==0 (sign is negative vs direct) + is_a0 = (a == 0).astype(float)[:, np.newaxis] + w5 = dMjbart_a / _clamp_product_denom(S_a, G_a) + term5 = -is_a0 * (RMTLj_cross * np.cumsum(w5, axis=1) - np.cumsum(RMTLj_cross * w5, axis=1)) + + # term6: only for a==0 (sign is positive vs direct) + w6 = Fj_cross * dMjbart_a / _clamp_product_denom(S_a, G_a) + term6 = is_a0 * (np.cumsum(s_row * w6, axis=1) - s_row * np.cumsum(w6, axis=1)) + + cut = term1 + term2 + term3 + term4 + term5 + term6 + return _interpolate_to_tau(cut, s, tau, ind) + + +def uif_diff_rmtlj(a, time, event, tau, bw, tilt, G_a0, G_a1, + S_a0, S_a1, Sj_a0, Sj_a1, cause=1, + time_grid=None, admin_cens=None): + """UIF transformation for RMTL total effect difference. + + Returns ``uif_a1 - uif_a0`` directly. + + Parameters + ---------- + a, time, event, tau, cause : see :func:`ipcw_cut_rmtlj`. + bw : ndarray (n,) + Balancing weights. + tilt : ndarray (n,) + Tilting weights. + G_a0, G_a1, S_a0, S_a1, Sj_a0, Sj_a1 : ndarray (n, ns) + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + bw = np.asarray(bw, dtype=float).ravel() + tilt = np.asarray(tilt, dtype=float).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + Sj_a0 = _clamp_survival(np.array(Sj_a0, dtype=float, copy=True)) + Sj_a1 = _clamp_survival(np.array(Sj_a1, dtype=float, copy=True)) + + ind_dict = _build_indicators(time, event, s, cause=cause) + Yt = ind_dict['Yt'] + dNjt = ind_dict['dNjt'] + dNt = ind_dict['dNt'] + + s_row = s[np.newaxis, :] + + uif_arms = [] + for arm, S, G, Sj in ((0, S_a0, G_a0, Sj_a0), (1, S_a1, G_a1, Sj_a1)): + Fj_dH = _incremental_hazard(Sj) + S_dH = _incremental_hazard(S) + Fj = _compute_cif_matrix(S, Fj_dH) + RMTLj = _cumulative_integral(Fj, ds) + SG = _clamp_product_denom(S, G) + + bw_arm = bw * (a == arm).astype(float) + bw_mat = bw_arm[:, np.newaxis] + tilt_mat = tilt[:, np.newaxis] + + dMjt = dNjt - Yt * Fj_dH + dMt = dNt - Yt * S_dH + + # term1: cause-j martingale correction + w1 = dMjt / G + term1 = bw_mat * (np.cumsum(s_row * w1, axis=1) - s_row * np.cumsum(w1, axis=1)) + + # term2: plug-in + term2 = tilt_mat * RMTLj + + # term3: overall martingale × RMTL + w3 = dMt / SG + term3 = bw_mat * RMTLj * np.cumsum(w3, axis=1) + + # term4: cumsum of RMTL × overall martingale + term4 = bw_mat * np.cumsum(RMTLj * w3, axis=1) + + # term5: CIF × overall martingale correction + w5 = Fj * dMt / SG + term5 = bw_mat * (np.cumsum(s_row * w5, axis=1) - s_row * np.cumsum(w5, axis=1)) + + uif_val = term1 + term2 - term3 + term4 + term5 + uif_arms.append(_interpolate_to_tau(uif_val, s, tau, ind)) + + return uif_arms[1] - uif_arms[0] + + +def uif_diff_rmtlj_sep_direct_astar1(a, ps, time, event, tau, bw, tilt, + G_a0, G_a1, S_a0, S_a1, + Sj_a0, Sj_a1, Sjbar_a0, Sjbar_a1, + cause=1, time_grid=None, admin_cens=None): + """UIF transformation for separable direct RMTL effect (a* = 1). + + Parameters + ---------- + a : ndarray (n,) + ps : ndarray (n,) + Propensity scores P(A=1|X). + time, event, tau, cause : see :func:`ipcw_cut_rmtlj`. + bw, tilt : ndarray (n,) + G_a0, G_a1, S_a0, S_a1, Sj_a0, Sj_a1, Sjbar_a0, Sjbar_a1 : ndarray (n, ns) + time_grid, admin_cens : see :func:`ipcw_cut_rmst`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + ps = np.asarray(ps, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + bw = np.asarray(bw, dtype=float).ravel() + tilt = np.asarray(tilt, dtype=float).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + Sj_a0 = _clamp_survival(np.array(Sj_a0, dtype=float, copy=True)) + Sj_a1 = _clamp_survival(np.array(Sj_a1, dtype=float, copy=True)) + Sjbar_a0 = _clamp_survival(np.array(Sjbar_a0, dtype=float, copy=True)) + Sjbar_a1 = _clamp_survival(np.array(Sjbar_a1, dtype=float, copy=True)) + + ind_dict = _build_indicators(time, event, s, cause=cause) + Yt = ind_dict['Yt'] + dNt = ind_dict['dNt'] + dNjt = ind_dict['dNjt'] + dNjbart = ind_dict['dNjbart'] + + s_row = s[np.newaxis, :] + + Fj_dH_a0 = _incremental_hazard(Sj_a0) + Fj_dH_a1 = _incremental_hazard(Sj_a1) + S_dH_a0 = _incremental_hazard(S_a0) + S_dH_a1 = _incremental_hazard(S_a1) + Fjbar_dH_a0 = _incremental_hazard(Sjbar_a0) + Fjbar_dH_a1 = _incremental_hazard(Sjbar_a1) + + Fj_a0 = _compute_cif_matrix(S_a0, Fj_dH_a0) + Fj_a1 = _compute_cif_matrix(S_a1, Fj_dH_a1) + S_cross = np.clip(Sj_a0 * Sjbar_a1, 1e-3, 1.0) + Fj_cross = _compute_cif_matrix(S_cross, Fj_dH_a0) + + RMTLj_a0 = _cumulative_integral(Fj_a0, ds) + RMTLj_a1 = _cumulative_integral(Fj_a1, ds) + RMTLj_cross = _cumulative_integral(Fj_cross, ds) + + bw_a0 = bw * (a == 0).astype(float) + bw_a1 = bw * (a == 1).astype(float) + + # --- a=0 arm --- + bw_a0_mat = bw_a0[:, np.newaxis] + + dMjt_a0 = dNjt - Yt * Fj_dH_a0 + dMt_a0 = dNt - Yt * S_dH_a0 + dMjbart_a0 = dNjbart - Yt * Fjbar_dH_a0 + + term1_0 = RMTLj_cross + + w2_0 = Sjbar_a1 * dMjt_a0 / _clamp_product_denom(Sjbar_a0, G_a0) + term2_0 = np.cumsum(s_row * w2_0, axis=1) - s_row * np.cumsum(w2_0, axis=1) + + w3_0 = Sjbar_a1 * dMt_a0 / _clamp_product_denom(Sjbar_a0, S_a0, G_a0) + term3_0 = -(RMTLj_a0 * np.cumsum(w3_0, axis=1) - np.cumsum(RMTLj_a0 * w3_0, axis=1)) + + w4_0 = Sjbar_a1 * Fj_a0 * dMt_a0 / _clamp_product_denom(Sjbar_a0, S_a0, G_a0) + term4_0 = np.cumsum(s_row * w4_0, axis=1) - s_row * np.cumsum(w4_0, axis=1) + + w5_0 = dMjbart_a0 / _clamp_product_denom(S_a0, G_a0) + term5_0 = RMTLj_cross * np.cumsum(w5_0, axis=1) - np.cumsum(RMTLj_cross * w5_0, axis=1) + + w6_0 = Fj_cross * dMjbart_a0 / _clamp_product_denom(S_a0, G_a0) + term6_0 = -(np.cumsum(s_row * w6_0, axis=1) - s_row * np.cumsum(w6_0, axis=1)) + + uif_a0 = (term1_0 + + bw_a0_mat * (term2_0 + term3_0 + term4_0) + + (bw_a0_mat - bw_a1[:, np.newaxis]) * (term5_0 + term6_0)) + uif_a0 = _interpolate_to_tau(uif_a0, s, tau, ind) + + # --- a=1 arm --- + bw_a1_mat = bw_a1[:, np.newaxis] + + dMjt_a1 = dNjt - Yt * Fj_dH_a1 + dMt_a1 = dNt - Yt * S_dH_a1 + + term1_1 = RMTLj_a1 + + w2_1 = dMjt_a1 / G_a1 + term2_1 = np.cumsum(s_row * w2_1, axis=1) - s_row * np.cumsum(w2_1, axis=1) + + w3_1 = dMt_a1 / _clamp_product_denom(S_a1, G_a1) + term3_1 = -(RMTLj_a1 * np.cumsum(w3_1, axis=1) - np.cumsum(RMTLj_a1 * w3_1, axis=1)) + + w4_1 = Fj_a1 * dMt_a1 / _clamp_product_denom(S_a1, G_a1) + term4_1 = np.cumsum(s_row * w4_1, axis=1) - s_row * np.cumsum(w4_1, axis=1) + + uif_a1 = term1_1 + bw_a1_mat * (term2_1 + term3_1 + term4_1) + uif_a1 = _interpolate_to_tau(uif_a1, s, tau, ind) + + return uif_a1 - uif_a0 + + +def uif_diff_rmtlj_sep_indirect_astar1(a, ps, time, event, tau, bw, tilt, + G_a0, G_a1, S_a0, S_a1, + Sj_a0, Sj_a1, Sjbar_a0, Sjbar_a1, + cause=1, time_grid=None, admin_cens=None): + """UIF transformation for separable indirect RMTL effect (a* = 1). + + Parameters + ---------- + Same as :func:`uif_diff_rmtlj_sep_direct_astar1`. + + Returns + ------- + pseudo_y : ndarray (n,) + """ + a = np.asarray(a, dtype=float).ravel() + ps = np.asarray(ps, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=int).ravel() + bw = np.asarray(bw, dtype=float).ravel() + tilt = np.asarray(tilt, dtype=float).ravel() + + s, ds, ind = _setup_time_grid(time, tau, time_grid, admin_cens) + n = len(time) + ns = len(s) + + S_a0 = _clamp_survival(np.array(S_a0, dtype=float, copy=True)) + S_a1 = _clamp_survival(np.array(S_a1, dtype=float, copy=True)) + G_a0 = _clamp_survival(np.array(G_a0, dtype=float, copy=True)) + G_a1 = _clamp_survival(np.array(G_a1, dtype=float, copy=True)) + Sj_a0 = _clamp_survival(np.array(Sj_a0, dtype=float, copy=True)) + Sj_a1 = _clamp_survival(np.array(Sj_a1, dtype=float, copy=True)) + Sjbar_a0 = _clamp_survival(np.array(Sjbar_a0, dtype=float, copy=True)) + Sjbar_a1 = _clamp_survival(np.array(Sjbar_a1, dtype=float, copy=True)) + + ind_dict = _build_indicators(time, event, s, cause=cause) + Yt = ind_dict['Yt'] + dNt = ind_dict['dNt'] + dNjt = ind_dict['dNjt'] + dNjbart = ind_dict['dNjbart'] + + s_row = s[np.newaxis, :] + + Fj_dH_a0 = _incremental_hazard(Sj_a0) + Fj_dH_a1 = _incremental_hazard(Sj_a1) + S_dH_a0 = _incremental_hazard(S_a0) + S_dH_a1 = _incremental_hazard(S_a1) + Fjbar_dH_a0 = _incremental_hazard(Sjbar_a0) + Fjbar_dH_a1 = _incremental_hazard(Sjbar_a1) + + Fj_a0 = _compute_cif_matrix(S_a0, Fj_dH_a0) + Fj_a1 = _compute_cif_matrix(S_a1, Fj_dH_a1) + S_cross = np.clip(Sj_a0 * Sjbar_a1, 1e-3, 1.0) + Fj_cross = _compute_cif_matrix(S_cross, Fj_dH_a0) + + RMTLj_a0 = _cumulative_integral(Fj_a0, ds) + RMTLj_a1 = _cumulative_integral(Fj_a1, ds) + RMTLj_cross = _cumulative_integral(Fj_cross, ds) + + bw_a0 = bw * (a == 0).astype(float) + bw_a1 = bw * (a == 1).astype(float) + + # --- a=0 arm --- + bw_a0_mat = bw_a0[:, np.newaxis] + + dMjt_a0 = dNjt - Yt * Fj_dH_a0 + dMt_a0 = dNt - Yt * S_dH_a0 + dMjbart_a0 = dNjbart - Yt * Fjbar_dH_a0 + + term1_0 = RMTLj_a0 + + # ratio (1 - Sjbar_a1/Sjbar_a0) for a=0 arm + ratio_0 = 1.0 - Sjbar_a1 / Sjbar_a0 + w2_0 = dMjt_a0 / G_a0 * ratio_0 + term2_0 = np.cumsum(s_row * w2_0, axis=1) - s_row * np.cumsum(w2_0, axis=1) + + w3_0 = dMt_a0 / _clamp_product_denom(S_a0, G_a0) * ratio_0 + term3_0 = -(RMTLj_a0 * np.cumsum(w3_0, axis=1) - np.cumsum(RMTLj_a0 * w3_0, axis=1)) + + w4_0 = Fj_a0 * dMt_a0 / _clamp_product_denom(S_a0, G_a0) * ratio_0 + term4_0 = np.cumsum(s_row * w4_0, axis=1) - s_row * np.cumsum(w4_0, axis=1) + + # term5, term6: sign is opposite from direct + w5_0 = dMjbart_a0 / _clamp_product_denom(S_a0, G_a0) + term5_0 = -(RMTLj_cross * np.cumsum(w5_0, axis=1) - np.cumsum(RMTLj_cross * w5_0, axis=1)) + + w6_0 = Fj_cross * dMjbart_a0 / _clamp_product_denom(S_a0, G_a0) + term6_0 = np.cumsum(s_row * w6_0, axis=1) - s_row * np.cumsum(w6_0, axis=1) + + uif_a0 = (term1_0 + + bw_a0_mat * (term2_0 + term3_0 + term4_0) + + (bw_a0_mat - bw_a1[:, np.newaxis]) * (term5_0 + term6_0)) + uif_a0 = _interpolate_to_tau(uif_a0, s, tau, ind) + + # --- a=1 arm --- + bw_a1_mat = bw_a1[:, np.newaxis] + + dMjt_a1 = dNjt - Yt * Fj_dH_a1 + dMt_a1 = dNt - Yt * S_dH_a1 + + term1_1 = RMTLj_cross + + # ratio (1 - Sjbar_a1/Sjbar_a1) = 0 for a=1 arm + # So terms 2-4 are zero. Only term1 contributes. + # But we still need to compute formally for consistency: + ratio_1 = 1.0 - Sjbar_a1 / Sjbar_a1 # = 0 + w2_1 = dMjt_a1 / G_a1 * ratio_1 + term2_1 = np.cumsum(s_row * w2_1, axis=1) - s_row * np.cumsum(w2_1, axis=1) + + w3_1 = dMt_a1 / _clamp_product_denom(S_a1, G_a1) * ratio_1 + term3_1 = -(RMTLj_a1 * np.cumsum(w3_1, axis=1) - np.cumsum(RMTLj_a1 * w3_1, axis=1)) + + w4_1 = Fj_a1 * dMt_a1 / _clamp_product_denom(S_a1, G_a1) * ratio_1 + term4_1 = np.cumsum(s_row * w4_1, axis=1) - s_row * np.cumsum(w4_1, axis=1) + + uif_a1 = term1_1 + bw_a1_mat * (term2_1 + term3_1 + term4_1) + uif_a1 = _interpolate_to_tau(uif_a1, s, tau, ind) + + return uif_a1 - uif_a0 diff --git a/econml/grf/__init__.py b/econml/grf/__init__.py index a9ccd6833..121e24ba0 100644 --- a/econml/grf/__init__.py +++ b/econml/grf/__init__.py @@ -11,12 +11,20 @@ https://arxiv.org/pdf/1610.01271.pdf """ -from ._criterion import LinearMomentGRFCriterion, LinearMomentGRFCriterionMSE -from .classes import CausalForest, CausalIVForest, RegressionForest, MultiOutputGRF - -__all__ = ["CausalForest", - "CausalIVForest", - "RegressionForest", - "MultiOutputGRF", - "LinearMomentGRFCriterion", - "LinearMomentGRFCriterionMSE"] +from ._criterion import LinearMomentGRFCriterion, LinearMomentGRFCriterionMSE +from .classes import CausalForest, CausalIVForest, RegressionForest, MultiOutputGRF +from ._causal_survival_forest import CausalSurvivalForest +from ._survival_forest import SurvivalForest, survival_forest +from ._translated_causal_forest import GRFCausalForest, causal_forest + +__all__ = ["CausalForest", + "CausalIVForest", + "RegressionForest", + "SurvivalForest", + "survival_forest", + "MultiOutputGRF", + "CausalSurvivalForest", + "GRFCausalForest", + "causal_forest", + "LinearMomentGRFCriterion", + "LinearMomentGRFCriterionMSE"] diff --git a/econml/grf/_causal_survival_forest.py b/econml/grf/_causal_survival_forest.py new file mode 100644 index 000000000..0cef066ec --- /dev/null +++ b/econml/grf/_causal_survival_forest.py @@ -0,0 +1,1522 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +""" +Causal Survival Forest for heterogeneous treatment effect estimation with +right-censored outcomes. + +Mirrors ``grf::causal_survival_forest`` from: + grf-master/r-package/grf/R/causal_survival_forest.R + +Strategy: estimate nuisance quantities (propensity, event survival, censoring +survival) in ``fit()``, then pass the pseudo-outcome and sample weights to a +dedicated internal causal-survival trainer. The public estimator coordinates +the survival-specific workflow while the internal trainer handles only the +final GRF stage. + +References +---------- +Cui, Y., Kosorok, M. R., Sverdrup, E., Wager, S., & Zhu, R. (2023). + Estimating heterogeneous treatment effects with right-censored data via + causal survival forests. + Journal of the Royal Statistical Society Series B, 85(2), 179-211. + https://doi.org/10.1093/jrsssb/qkac085 +""" + +import warnings + +import numpy as np +from sklearn.base import BaseEstimator +from sklearn.base import clone as sk_clone +from sklearn.model_selection import GroupKFold, KFold, StratifiedKFold +from sklearn.utils import check_array +from sklearn.utils.validation import check_is_fitted + +from .classes import RegressionForest +from ._survival_forest import SurvivalForest, _cluster_weight_vector + + +_PROPENSITY_CLIP = 1e-3 + + +class _CausalSurvivalTreeNode: + def __init__(self, *, is_leaf, theta=0.0, intercept=0.0, node_id=0, + feature=-1, threshold=0.0, left=None, right=None): + self.is_leaf = is_leaf + self.theta = float(theta) + self.intercept = float(intercept) + self.node_id = int(node_id) + self.feature = int(feature) + self.threshold = float(threshold) + self.left = left + self.right = right + + +def _make_sksurv_y(time, event): + return np.array( + [(bool(e), float(t)) for e, t in zip(event, time)], + dtype=[("event", bool), ("time", float)], + ) + + +def _predict_treatment_model(model, X): + """Predict propensities from a fitted treatment nuisance model.""" + if hasattr(model, "predict_proba"): + proba = model.predict_proba(X) + proba = np.asarray(proba) + if proba.ndim == 2 and proba.shape[1] > 1: + return proba[:, 1] + return np.asarray(model.predict(X)).ravel() + + +def _extract_oob_treatment_predictions(model, X): + """Extract OOB treatment predictions when the fitted model exposes them.""" + if hasattr(model, "oob_decision_function_"): + proba = np.asarray(model.oob_decision_function_) + if proba.ndim == 2 and proba.shape[1] > 1: + return proba[:, 1] + if hasattr(model, "oob_prediction_"): + return np.asarray(model.oob_prediction_).ravel() + if hasattr(model, "oob_predict"): + try: + return np.asarray(model.oob_predict(X)).ravel() + except Exception: + return None + return None + + +def _build_csf_folds(X, T, event, *, random_state, n_splits=5): + """Build held-out folds for nuisance estimation. + + We mirror grf's OOB nuisance philosophy with explicit out-of-fold predictions + when the nuisance model does not natively support OOB prediction. + """ + n = X.shape[0] + n_splits = max(2, min(int(n_splits), n)) + dummy = np.zeros(n) + + joint = np.array([f"{int(t)}_{int(e)}" for t, e in zip(T, event)], dtype=object) + _, joint_counts = np.unique(joint, return_counts=True) + if joint_counts.size > 0 and np.min(joint_counts) >= n_splits: + splitter = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state) + return list(splitter.split(dummy, joint)) + + treat = np.asarray(T).astype(int) + _, treat_counts = np.unique(treat, return_counts=True) + if treat_counts.size > 0 and np.min(treat_counts) >= n_splits: + splitter = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state) + return list(splitter.split(dummy, treat)) + + splitter = KFold(n_splits=n_splits, shuffle=True, random_state=random_state) + return list(splitter.split(dummy)) + + +def _build_csf_cluster_folds(X, T, event, clusters, *, n_splits=5): + n = X.shape[0] + groups = np.asarray(clusters) + unique_groups = np.unique(groups) + n_splits = max(2, min(int(n_splits), unique_groups.shape[0])) + dummy = np.zeros(n) + splitter = GroupKFold(n_splits=n_splits) + return list(splitter.split(dummy, groups=groups)) + + +def _cluster_subsample_indices(cluster_info, frac, rng, *, equalize_cluster_weights): + unique_clusters, inverse, counts = cluster_info + n_clusters = len(unique_clusters) + n_sub_clusters = max(1, int(np.ceil(frac * n_clusters))) + n_sub_clusters = min(n_sub_clusters, n_clusters) + sampled_cluster_pos = rng.choice(n_clusters, size=n_sub_clusters, replace=False) + sampled_pos_set = set(sampled_cluster_pos.tolist()) + if equalize_cluster_weights: + k = int(np.min(counts)) + pieces = [] + for pos in sampled_cluster_pos: + members = np.flatnonzero(inverse == pos) + pieces.append(rng.choice(members, size=k, replace=False)) + sample_idx = np.concatenate(pieces) + else: + sample_idx = np.concatenate([np.flatnonzero(inverse == pos) for pos in sampled_cluster_pos]) + unsampled = np.flatnonzero(np.array([pos not in sampled_pos_set for pos in inverse])) + return sample_idx, unsampled + + +def _crossfit_treatment_predictions(model, X, T, *, sample_weight=None, random_state, n_splits=5, clusters=None): + """Estimate W_hat with OOB predictions when available, else explicit OOF.""" + fitted_model = sk_clone(model) + if sample_weight is None: + fitted_model.fit(X, T) + else: + fitted_model.fit(X, T, sample_weight=sample_weight) + + W_hat = _extract_oob_treatment_predictions(fitted_model, X) + + if W_hat is None or np.any(~np.isfinite(W_hat)): + if clusters is None: + folds = _build_csf_folds( + X, T, np.zeros_like(T), random_state=random_state, n_splits=n_splits + ) + else: + folds = _build_csf_cluster_folds( + X, T, np.zeros_like(T), clusters, n_splits=n_splits + ) + W_hat = np.empty(X.shape[0], dtype=float) + for train_idx, test_idx in folds: + fold_model = sk_clone(model) + if sample_weight is None: + fold_model.fit(X[train_idx], T[train_idx]) + else: + fold_model.fit(X[train_idx], T[train_idx], sample_weight=sample_weight[train_idx]) + W_hat[test_idx] = _predict_treatment_model(fold_model, X[test_idx]) + + return fitted_model, np.clip(W_hat, _PROPENSITY_CLIP, 1 - _PROPENSITY_CLIP) + + +def _fit_survival_nuisance_model(model, X, time, event, *, sample_weight=None): + y_struct = _make_sksurv_y(time, event) + try: + if sample_weight is not None: + return model.fit(X, time, event, sample_weight=sample_weight) + return model.fit(X, time, event) + except TypeError: + if sample_weight is not None: + try: + return model.fit(X, y_struct, sample_weight=sample_weight) + except TypeError: + pass + return model.fit(X, y_struct) + + +def _expected_survival_rmst(S_hat, Y_grid): + """Compute E[min(T, max(Y_grid)) | X] = integral_0^max S(t|X) dt. + + Mirrors R's ``expected_survival(S.hat, Y.grid)``. + + Parameters + ---------- + S_hat : ndarray (n, ns) + Y_grid : ndarray (ns,) + + Returns + ------- + ndarray (n,) + """ + grid_diff = np.diff(np.concatenate([[0.0], Y_grid, [Y_grid[-1]]])) + # prepend column of 1s (S(0)=1) + S_aug = np.hstack([np.ones((S_hat.shape[0], 1)), S_hat]) + return S_aug @ grid_diff + + +def _compute_psi(S_hat, C_hat, C_Y_hat, Y_hat, W_centered, + D, fY, Y_index, Y_grid, target, horizon): + """Compute the GRF causal survival pseudo-outcome (numerator & denominator). + + Direct port of ``compute_psi`` from: + grf-master/r-package/grf/R/causal_survival_forest.R lines 522-577 + + Parameters + ---------- + S_hat : ndarray (n, ns) event survival S(t|X,W) on Y_grid + C_hat : ndarray (n, ns) censoring survival C(t|X,W) on Y_grid + C_Y_hat : ndarray (n,) C(Y_i|X_i,W_i) — censoring prob at obs time + Y_hat : ndarray (n,) E[f(T)|X] + W_centered: ndarray (n,) W - W_hat + D : ndarray (n,) event indicator (possibly modified for RMST) + fY : ndarray (n,) f(T) = min(T, horizon) for RMST, 1{T>horizon} for SP + Y_index : ndarray (n,) int index into Y_grid of each obs time + Y_grid : ndarray (ns,) + target : str "RMST" or "survival.probability" + horizon : float + + Returns + ------- + numerator : ndarray (n,) + denominator : ndarray (n,) + """ + n, ns = S_hat.shape + + # --- Compute Q_hat(t, X) = E[f(T) | X, W, T > t] --- + if target == "RMST": + # Backward cumsum approach (mirrors R) + Y_diff = np.diff(np.concatenate([[0.0], Y_grid])) # (ns,) + # dot_products[i, j] = S_hat[i, j] * Y_diff[j+1] for j in 0..ns-2 + dot_products = S_hat[:, :-1] * Y_diff[1:] # (n, ns-1) + Q_hat = np.empty((n, ns)) + Q_hat[:, 0] = dot_products.sum(axis=1) + for j in range(1, ns - 1): + Q_hat[:, j] = Q_hat[:, j - 1] - dot_products[:, j - 1] + # Divide by S_hat and add back t + Q_hat = Q_hat / S_hat + Y_grid[np.newaxis, :] + Q_hat[:, -1] = Y_grid[-1] + else: + # survival.probability: Q(t, X) = P(T > horizon | T > t) = S(horizon) / S(t) + horizon_idx = np.searchsorted(Y_grid, horizon, side='right') - 1 + horizon_idx = np.clip(horizon_idx, 0, ns - 1) + Q_hat = S_hat[:, horizon_idx:horizon_idx + 1] / S_hat # (n, ns) + Q_hat[:, horizon_idx:] = 1.0 + + # Pick out Q(Y_i, X_i) + Q_Y_hat = Q_hat[np.arange(n), Y_index] # (n,) + + # --- numerator_one --- + numerator_one = ( + (D * (fY - Y_hat) + (1 - D) * (Q_Y_hat - Y_hat)) + * W_centered / C_Y_hat + ) + + # --- numerator_two: integral correction for censoring --- + # dlambda_C_hat = -d log C_hat (forward difference) + # log_surv_C prepended with col of 0 (log 1 = 0) + log_surv_C = np.hstack([np.zeros((n, 1)), -np.log(C_hat)]) # (n, ns+1) + dlambda_C_hat = log_surv_C[:, 1:] - log_surv_C[:, :-1] # (n, ns) + + integrand = dlambda_C_hat / C_hat * (Q_hat - Y_hat[:, np.newaxis]) # (n, ns) + + # For each sample, sum integrand up to Y_index[i] (inclusive) + # Use cumsum then index — vectorised equivalent of R's loop + cum_integrand = np.cumsum(integrand, axis=1) # (n, ns) + integral_at_Yi = cum_integrand[np.arange(n), Y_index] # (n,) + numerator_two = integral_at_Yi * W_centered + + numerator = numerator_one - numerator_two + denominator = W_centered ** 2 # simplifies in GRF paper + + return numerator, denominator + + +def _csf_candidate_thresholds(values): + uniq = np.unique(values) + if uniq.size <= 1: + return np.array([], dtype=float) + mids = (uniq[:-1] + uniq[1:]) / 2.0 + if mids.size <= 32: + return mids + return np.unique(np.quantile(mids, np.linspace(0.05, 0.95, 16))) + + +def _fit_default_regression_forest(X, T, *, sample_weight, n_estimators, max_features, + max_samples, min_balancedness_tol, random_state, + min_samples_leaf=5, clusters=None, + equalize_cluster_weights=False): + if clusters is None: + model = RegressionForest( + n_estimators=n_estimators, + max_features=max_features, + max_samples=max_samples, + min_balancedness_tol=min_balancedness_tol, + min_samples_leaf=min_samples_leaf, + inference=False, + random_state=random_state, + ) + if sample_weight is None: + model.fit(X, T) + else: + model.fit(X, T, sample_weight=sample_weight) + oob = np.asarray(model.oob_predict(X)).ravel() + else: + model = _ClusteredTreatmentForest( + n_estimators=n_estimators, + max_features=max_features, + sample_fraction=max_samples, + min_balancedness_tol=min_balancedness_tol, + min_samples_leaf=min_samples_leaf, + random_state=random_state, + clusters=clusters, + equalize_cluster_weights=equalize_cluster_weights, + ) + if sample_weight is None: + model.fit(X, T) + else: + model.fit(X, T, sample_weight=sample_weight) + oob = np.asarray(model.oob_predict(X)).ravel() + mse = np.mean((T - oob) ** 2) + return model, mse + + +def _draw_treatment_tuning_candidates(X, *, defaults, tune_parameters, num_draws, random_state): + p = X.shape[1] + rng = np.random.RandomState(random_state) + params = [] + for _ in range(max(1, int(num_draws))): + draw = dict(defaults) + if "sample.fraction" in tune_parameters: + draw["sample.fraction"] = float(rng.uniform(0.05, 0.5)) + if "mtry" in tune_parameters: + hi = max(1, p) + lo = 1 + draw["mtry"] = int(rng.randint(lo, hi + 1)) + if "min.node.size" in tune_parameters: + draw["min.node.size"] = int(rng.choice([3, 5, 7, 10, 15])) + if "alpha" in tune_parameters: + draw["alpha"] = float(rng.choice([0.01, 0.025, 0.05, 0.1])) + params.append(draw) + + deduped = [] + seen = set() + for draw in [defaults] + params: + key = ( + round(float(draw["sample.fraction"]), 6), + int(draw["mtry"]), + int(draw["min.node.size"]), + round(float(draw["alpha"]), 6), + ) + if key not in seen: + seen.add(key) + deduped.append(draw) + return deduped + + +def _tune_default_treatment_forest(X, T, *, sample_weight, n_estimators, max_features, + max_samples, min_balancedness_tol, random_state, + tune_parameters, clusters=None, + tune_num_draws=32, tune_num_reps=1, tune_num_trees=32, + equalize_cluster_weights=False): + default_mtry = max_features if isinstance(max_features, int) else max(1, int(np.ceil(np.sqrt(X.shape[1])))) + defaults = { + "sample.fraction": max_samples, + "mtry": default_mtry, + "min.node.size": 5, + "alpha": 0.5 - min_balancedness_tol, + "imbalance.penalty": 0.0, + } + if tune_parameters == "all": + params_to_tune = ["sample.fraction", "mtry", "min.node.size", "alpha"] + else: + allowed = {"sample.fraction", "mtry", "min.node.size", "honesty.fraction", + "honesty.prune.leaves", "alpha", "imbalance.penalty"} + params_to_tune = [p for p in np.atleast_1d(tune_parameters).tolist() if p in allowed] + + tunable_keys = [k for k in ["sample.fraction", "mtry", "min.node.size", "alpha"] if k in params_to_tune] + if not tunable_keys: + model, _ = _fit_default_regression_forest( + X, T, + sample_weight=sample_weight, + n_estimators=n_estimators, + max_features=max_features, + max_samples=max_samples, + min_balancedness_tol=min_balancedness_tol, + random_state=random_state, + min_samples_leaf=5, + clusters=clusters, + equalize_cluster_weights=equalize_cluster_weights, + ) + return model, defaults, { + "metric": "oob_mse", + "num.draws": 1, + "num.reps": 1, + "num.trees": int(n_estimators), + "results": [dict(params=dict(defaults), mean_oob_mse=np.nan, rep_oob_mse=[])], + } + + best_model = None + best_params = None + best_score = np.inf + results = [] + candidate_params = _draw_treatment_tuning_candidates( + X, + defaults=defaults, + tune_parameters=tunable_keys, + num_draws=tune_num_draws, + random_state=random_state, + ) + tune_trees = max(12, int(tune_num_trees)) + for draw_id, params in enumerate(candidate_params): + rep_scores = [] + for rep in range(max(1, int(tune_num_reps))): + _, mse = _fit_default_regression_forest( + X, T, + sample_weight=sample_weight, + n_estimators=tune_trees, + max_features=params["mtry"], + max_samples=params["sample.fraction"], + min_balancedness_tol=0.5 - params["alpha"], + random_state=None if random_state is None else int(random_state + 1009 * draw_id + 37 * rep), + min_samples_leaf=params["min.node.size"], + clusters=clusters, + equalize_cluster_weights=equalize_cluster_weights, + ) + rep_scores.append(float(mse)) + mean_score = float(np.mean(rep_scores)) + results.append({ + "params": dict(params), + "mean_oob_mse": mean_score, + "rep_oob_mse": rep_scores, + }) + if mean_score < best_score: + best_score = mean_score + best_params = dict(params) + + best_model, _ = _fit_default_regression_forest( + X, T, + sample_weight=sample_weight, + n_estimators=n_estimators, + max_features=best_params["mtry"], + max_samples=best_params["sample.fraction"], + min_balancedness_tol=0.5 - best_params["alpha"], + random_state=random_state, + min_samples_leaf=best_params["min.node.size"], + clusters=clusters, + equalize_cluster_weights=equalize_cluster_weights, + ) + tuning_output = { + "metric": "oob_mse", + "num.draws": len(candidate_params), + "num.reps": max(1, int(tune_num_reps)), + "num.trees": tune_trees, + "results": results, + "best.params": dict(best_params), + "best.mean_oob_mse": float(best_score), + } + return best_model, best_params, tuning_output + + +class _ClusteredTreatmentForest(BaseEstimator): + """Cluster-aware treatment nuisance using cluster-level subsampling and OOB aggregation.""" + + def __init__(self, *, n_estimators, max_features, sample_fraction, + min_balancedness_tol, min_samples_leaf, random_state, + clusters, equalize_cluster_weights): + self.n_estimators = n_estimators + self.max_features = max_features + self.sample_fraction = sample_fraction + self.min_balancedness_tol = min_balancedness_tol + self.min_samples_leaf = min_samples_leaf + self.random_state = random_state + self.clusters = clusters + self.equalize_cluster_weights = equalize_cluster_weights + + def fit(self, X, T, sample_weight=None): + X = check_array(np.asarray(X, dtype=float)) + T = np.asarray(T, dtype=float).ravel() + n = X.shape[0] + self.X_train_ = np.array(X, copy=True) + self.T_train_ = np.array(T, copy=True) + _, self.cluster_info_ = _cluster_weight_vector(self.clusters, self.equalize_cluster_weights, n) + rng = np.random.RandomState(self.random_state) + self.estimators_ = [] + self.subsample_indices_ = [] + oob_sum = np.zeros(n, dtype=float) + oob_count = np.zeros(n, dtype=int) + + for _ in range(self.n_estimators): + sample_idx, unsampled = _cluster_subsample_indices( + self.cluster_info_, self.sample_fraction, rng, + equalize_cluster_weights=self.equalize_cluster_weights + ) + est = RegressionForest( + n_estimators=1, + max_features=self.max_features, + max_samples=1.0, + min_balancedness_tol=self.min_balancedness_tol, + min_samples_leaf=self.min_samples_leaf, + honest=True, + inference=False, + random_state=rng.randint(np.iinfo(np.int32).max), + ) + if sample_weight is None: + est.fit(X[sample_idx], T[sample_idx]) + else: + est.fit(X[sample_idx], T[sample_idx], sample_weight=np.asarray(sample_weight)[sample_idx]) + self.estimators_.append(est) + self.subsample_indices_.append(sample_idx) + if unsampled.size > 0: + pred = np.asarray(est.predict(X[unsampled])).ravel() + oob_sum[unsampled] += pred + oob_count[unsampled] += 1 + + self.oob_prediction_ = np.empty(n, dtype=float) + mask = oob_count > 0 + if np.any(mask): + self.oob_prediction_[mask] = oob_sum[mask] / oob_count[mask] + if np.any(~mask): + self.oob_prediction_[~mask] = self.predict(X[~mask]) + return self + + def predict(self, X): + X = check_array(np.asarray(X, dtype=float)) + preds = np.vstack([np.asarray(est.predict(X)).ravel() for est in self.estimators_]) + return np.mean(preds, axis=0) + + def oob_predict(self, Xtrain): + Xtrain = check_array(np.asarray(Xtrain, dtype=float)) + if Xtrain.shape != self.X_train_.shape or not np.array_equal(Xtrain, self.X_train_): + raise ValueError("oob_predict is only defined on the training sample.") + return np.array(self.oob_prediction_, copy=True) + + +def _weighted_leaf_params(w, y, sample_weight, fit_intercept): + if y.size == 0: + return np.nan, np.nan + sw = np.ones_like(w, dtype=float) if sample_weight is None else np.asarray(sample_weight, dtype=float) + total = np.sum(sw) + if total <= 1e-10: + return np.nan, np.nan + theta = np.sum(sw * y) / total + return theta, 0.0 + + +def _weighted_leaf_loss(y, sample_weight): + if y.size == 0: + return 0.0 + sw = np.ones_like(y, dtype=float) if sample_weight is None else np.asarray(sample_weight, dtype=float) + total = np.sum(sw) + if total <= 1e-10: + return 0.0 + mean = np.sum(sw * y) / total + resid = y - mean + return np.sum(sw * resid * resid) + + +class _CausalSurvivalTree: + def __init__(self, *, mtry, min_samples_split, min_samples_leaf, max_depth, + min_balancedness_tol, fit_intercept, honest, honesty_fraction, + honesty_prune_leaves, alpha, imbalance_penalty, stabilize_splits, + random_state): + self.mtry = mtry + self.min_samples_split = min_samples_split + self.min_samples_leaf = min_samples_leaf + self.max_depth = max_depth + self.min_balancedness_tol = min_balancedness_tol + self.fit_intercept = fit_intercept + self.honest = honest + self.honesty_fraction = honesty_fraction + self.honesty_prune_leaves = honesty_prune_leaves + self.alpha = alpha + self.imbalance_penalty = imbalance_penalty + self.stabilize_splits = stabilize_splits + self.random_state = random_state + self._next_node_id = 0 + self.feature_importances_ = None + + def fit(self, X, W, y, sample_weight, *, treatment_raw=None, censor=None): + n = X.shape[0] + self.treatment_raw_ = None if treatment_raw is None else np.asarray(treatment_raw, dtype=int) + self.censor_ = None if censor is None else np.asarray(censor, dtype=int) + if self.honest: + perm = self.random_state.permutation(n) + split_n = int(np.floor(n * self.honesty_fraction)) + est_n = n - split_n + if split_n <= 0 or est_n <= 0: + raise ValueError("honesty_fraction leaves no split or estimation sample.") + split_idx = perm[:split_n] + est_idx = perm[split_n:] + else: + split_idx = np.arange(n) + est_idx = np.arange(n) + self.feature_importances_ = np.zeros(X.shape[1], dtype=float) + self.root_ = self._grow(X, W, y, sample_weight, split_idx, est_idx, depth=0) + return self + + def _new_node_id(self): + node_id = self._next_node_id + self._next_node_id += 1 + return node_id + + def _best_split(self, X, W, y, sample_weight, split_idx): + n = split_idx.size + min_frac = max(0.0, 0.5 - self.min_balancedness_tol) + features = self.random_state.choice(X.shape[1], size=min(self.mtry, X.shape[1]), replace=False) + best = (-np.inf, None, None) + parent_loss = _weighted_leaf_loss(y[split_idx], sample_weight[split_idx]) + for feature in features: + thresholds = _csf_candidate_thresholds(X[split_idx, feature]) + for threshold in thresholds: + left = split_idx[X[split_idx, feature] <= threshold] + right = split_idx[X[split_idx, feature] > threshold] + if left.size < self.min_samples_leaf or right.size < self.min_samples_leaf: + continue + if min(left.size, right.size) / n < min_frac: + continue + imbalance = abs(left.size - right.size) / max(n, 1) + if self.stabilize_splits: + for labels in (self.treatment_raw_, self.censor_): + if labels is None: + continue + parent_labels = labels[split_idx] + for cls in np.unique(parent_labels): + parent_count = np.sum(parent_labels == cls) + if parent_count == 0: + continue + left_count = np.sum(labels[left] == cls) + right_count = np.sum(labels[right] == cls) + min_required = max(1, int(np.floor(self.alpha * n))) + if min(left_count, right_count) < min_required: + imbalance = np.inf + break + imbalance += abs(left_count - right_count) / parent_count + if not np.isfinite(imbalance): + break + if not np.isfinite(imbalance): + continue + left_loss = _weighted_leaf_loss(y[left], sample_weight[left]) + right_loss = _weighted_leaf_loss(y[right], sample_weight[right]) + score = parent_loss - left_loss - right_loss - self.imbalance_penalty * imbalance + if score > best[0]: + best = (score, feature, threshold) + return best + + def _grow(self, X, W, y, sample_weight, split_idx, est_idx, depth): + theta, intercept = _weighted_leaf_params( + W[est_idx], y[est_idx], sample_weight[est_idx], self.fit_intercept + ) + node_id = self._new_node_id() + if split_idx.size < self.min_samples_split or split_idx.size < 2 * self.min_samples_leaf: + return _CausalSurvivalTreeNode(is_leaf=True, theta=theta, intercept=intercept, node_id=node_id) + if self.max_depth is not None and depth >= self.max_depth: + return _CausalSurvivalTreeNode(is_leaf=True, theta=theta, intercept=intercept, node_id=node_id) + + score, feature, threshold = self._best_split(X, W, y, sample_weight, split_idx) + if feature is None or not np.isfinite(score) or score <= 0: + return _CausalSurvivalTreeNode(is_leaf=True, theta=theta, intercept=intercept, node_id=node_id) + + split_left = split_idx[X[split_idx, feature] <= threshold] + split_right = split_idx[X[split_idx, feature] > threshold] + est_left = est_idx[X[est_idx, feature] <= threshold] + est_right = est_idx[X[est_idx, feature] > threshold] + if self.honest and self.honesty_prune_leaves and (est_left.size == 0 or est_right.size == 0): + return _CausalSurvivalTreeNode(is_leaf=True, theta=theta, intercept=intercept, node_id=node_id) + + self.feature_importances_[feature] += score / ((1 + depth) ** 2) + left = self._grow(X, W, y, sample_weight, split_left, est_left, depth + 1) + right = self._grow(X, W, y, sample_weight, split_right, est_right, depth + 1) + return _CausalSurvivalTreeNode( + is_leaf=False, theta=theta, intercept=intercept, node_id=node_id, + feature=feature, threshold=float(threshold), left=left, right=right + ) + + def _leaf_for_row(self, x): + node = self.root_ + while not node.is_leaf: + node = node.left if x[node.feature] <= node.threshold else node.right + return node + + def predict(self, X): + out = np.empty(X.shape[0], dtype=float) + for i, x in enumerate(X): + node = self._leaf_for_row(x) + out[i] = node.theta + return out + + def apply(self, X): + out = np.empty(X.shape[0], dtype=int) + for i, x in enumerate(X): + out[i] = self._leaf_for_row(x).node_id + return out + + +class _CausalSurvivalForestTrainer(BaseEstimator): + """Dedicated final-stage trainer for the causal survival forest.""" + + def __init__(self, *, n_estimators=100, max_depth=None, min_samples_split=10, + min_samples_leaf=5, max_features="auto", max_samples=0.45, + min_balancedness_tol=0.45, honest=True, honesty_fraction=0.5, + honesty_prune_leaves=True, inference=True, + fit_intercept=True, subforest_size=4, n_jobs=-1, random_state=None, verbose=0, + criterion="mse", min_weight_fraction_leaf=0.0, + min_var_fraction_leaf=None, min_var_leaf_on_val=False, + min_impurity_decrease=0.0, alpha=0.05, imbalance_penalty=0.0, + stabilize_splits=True, clusters=None, equalize_cluster_weights=False): + self.n_estimators = n_estimators + self.max_depth = max_depth + self.min_samples_split = min_samples_split + self.min_samples_leaf = min_samples_leaf + self.max_features = max_features + self.max_samples = max_samples + self.min_balancedness_tol = min_balancedness_tol + self.honest = honest + self.honesty_fraction = honesty_fraction + self.honesty_prune_leaves = honesty_prune_leaves + self.inference = inference + self.fit_intercept = fit_intercept + self.subforest_size = subforest_size + self.n_jobs = n_jobs + self.random_state = random_state + self.verbose = verbose + self.criterion = criterion + self.min_weight_fraction_leaf = min_weight_fraction_leaf + self.min_var_fraction_leaf = min_var_fraction_leaf + self.min_var_leaf_on_val = min_var_leaf_on_val + self.min_impurity_decrease = min_impurity_decrease + self.alpha = alpha + self.imbalance_penalty = imbalance_penalty + self.stabilize_splits = stabilize_splits + self.clusters = clusters + self.equalize_cluster_weights = equalize_cluster_weights + + def _subsample_indices(self, n, rng): + if self.cluster_info_ is None: + n_sub = max(1, int(np.ceil(self.max_samples * n))) + n_sub = min(n_sub, n) + sample_idx = rng.choice(n, size=n_sub, replace=False) + unsampled = np.setdiff1d(np.arange(n), sample_idx, assume_unique=True) + return sample_idx, unsampled + + unique_clusters, inverse, counts = self.cluster_info_ + n_clusters = len(unique_clusters) + n_sub_clusters = max(1, int(np.ceil(self.max_samples * n_clusters))) + n_sub_clusters = min(n_sub_clusters, n_clusters) + sampled_cluster_pos = rng.choice(n_clusters, size=n_sub_clusters, replace=False) + sampled_pos_set = set(sampled_cluster_pos.tolist()) + if self.equalize_cluster_weights: + k = int(np.min(counts)) + pieces = [] + for pos in sampled_cluster_pos: + members = np.flatnonzero(inverse == pos) + pieces.append(rng.choice(members, size=k, replace=False)) + sample_idx = np.concatenate(pieces) + else: + sample_idx = np.concatenate([np.flatnonzero(inverse == pos) for pos in sampled_cluster_pos]) + unsampled = np.flatnonzero(np.array([pos not in sampled_pos_set for pos in inverse])) + return sample_idx, unsampled + + def fit(self, X, W, y, *, sample_weight=None, treatment_raw=None, censor=None): + X = check_array(np.asarray(X, dtype=float)) + W = np.asarray(W, dtype=float).reshape(-1) + y = np.asarray(y, dtype=float).reshape(-1) + sw = np.ones(X.shape[0], dtype=float) if sample_weight is None else np.asarray(sample_weight, dtype=float).reshape(-1) + n = X.shape[0] + self.X_train_ = np.array(X, copy=True) + self.W_train_ = np.array(W, copy=True) + self.y_train_ = np.array(y, copy=True) + self.sample_weight_ = np.array(sw, copy=True) + self.treatment_raw_ = None if treatment_raw is None else np.asarray(treatment_raw, dtype=int).reshape(-1) + self.censor_ = None if censor is None else np.asarray(censor, dtype=int).reshape(-1) + self.n_features_in_ = X.shape[1] + self.mtry_ = min(self.n_features_in_, int(np.ceil(np.sqrt(self.n_features_in_) + 20))) if self.max_features == "auto" else int(self.max_features) + self.n_outputs_ = 1 + self.n_relevant_outputs_ = 1 + _, self.cluster_info_ = _cluster_weight_vector(self.clusters, self.equalize_cluster_weights, n) + if self.inference and self.n_estimators % self.subforest_size != 0: + raise ValueError("n_estimators must be divisible by subforest_size when inference=True.") + + rng = np.random.RandomState(self.random_state) + self.estimators_ = [] + self.subsample_indices_ = [] + oob_sum = np.zeros(n, dtype=float) + oob_count = np.zeros(n, dtype=int) + + for _ in range(self.n_estimators): + sample_idx, unsampled = self._subsample_indices(n, rng) + tree = _CausalSurvivalTree( + mtry=self.mtry_, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + max_depth=self.max_depth, + min_balancedness_tol=self.min_balancedness_tol, + fit_intercept=self.fit_intercept, + honest=self.honest, + honesty_fraction=self.honesty_fraction, + honesty_prune_leaves=self.honesty_prune_leaves, + alpha=self.alpha, + imbalance_penalty=self.imbalance_penalty, + stabilize_splits=self.stabilize_splits, + random_state=np.random.RandomState(rng.randint(np.iinfo(np.int32).max)), + ) + tr_raw = None if self.treatment_raw_ is None else self.treatment_raw_[sample_idx] + censor = None if self.censor_ is None else self.censor_[sample_idx] + tree.fit(X[sample_idx], W[sample_idx], y[sample_idx], sw[sample_idx], + treatment_raw=tr_raw, censor=censor) + self.estimators_.append(tree) + self.subsample_indices_.append(sample_idx) + if unsampled.size > 0: + preds = tree.predict(X[unsampled]) + valid = np.isfinite(preds) + if np.any(valid): + oob_sum[unsampled[valid]] += preds[valid] + oob_count[unsampled[valid]] += 1 + + self.oob_predictions_ = np.empty(n, dtype=float) + mask = oob_count > 0 + self.oob_predictions_[mask] = oob_sum[mask] / oob_count[mask] + if np.any(~mask): + self.oob_predictions_[~mask] = self.predict(X[~mask]).reshape(-1) + + self.feature_importances_ = np.mean( + [tree.feature_importances_ for tree in self.estimators_], axis=0 + ) + total = np.sum(self.feature_importances_) + if total > 0: + self.feature_importances_ = self.feature_importances_ / total + + if self.inference: + self.subforest_groups_ = [ + self.estimators_[i:i + self.subforest_size] + for i in range(0, len(self.estimators_), self.subforest_size) + ] + else: + self.subforest_groups_ = None + return self + + def predict(self, X, interval=False, alpha=0.05): + X = check_array(np.asarray(X, dtype=float)) + tree_preds = np.vstack([tree.predict(X) for tree in self.estimators_]) + finite = np.isfinite(tree_preds) + counts = np.maximum(np.sum(finite, axis=0), 1) + preds = (np.sum(np.where(finite, tree_preds, 0.0), axis=0) / counts).reshape(-1, 1) + if not interval: + return preds + _, var = self.predict_and_var(X) + std = np.sqrt(np.clip(var[:, 0, 0], 0.0, None)) + z = 1.959963984540054 + return preds, (preds[:, 0] - z * std).reshape(-1, 1), (preds[:, 0] + z * std).reshape(-1, 1) + + def predict_and_var(self, X): + X = check_array(np.asarray(X, dtype=float)) + point = self.predict(X).reshape(-1, 1) + if not self.inference or self.subforest_groups_ is None or len(self.subforest_groups_) <= 1: + var = np.zeros((X.shape[0], 1, 1), dtype=float) + return point, var + group_preds = [] + for group in self.subforest_groups_: + gp = np.vstack([tree.predict(X) for tree in group]) + finite = np.isfinite(gp) + counts = np.maximum(np.sum(finite, axis=0), 1) + group_preds.append(np.sum(np.where(finite, gp, 0.0), axis=0) / counts) + group_preds = np.stack(group_preds, axis=0) + var_vec = np.var(group_preds, axis=0, ddof=1) / group_preds.shape[0] + return point, var_vec.reshape(-1, 1, 1) + + def predict_var(self, X): + return self.predict_and_var(X)[1] + + def predict_projection_and_var(self, X, projector): + proj = np.asarray(projector, dtype=float).reshape(-1, 1) + point, var = self.predict_and_var(X) + return point * proj, var * (proj[:, :, None] ** 2) + + def predict_projection(self, X, projector): + return self.predict_projection_and_var(X, projector)[0] + + def predict_projection_var(self, X, projector): + return self.predict_projection_and_var(X, projector)[1] + + def oob_predict(self, Xtrain): + Xtrain = check_array(np.asarray(Xtrain, dtype=float)) + if Xtrain.shape != self.X_train_.shape or not np.array_equal(Xtrain, self.X_train_): + raise ValueError("oob_predict is only defined on the training sample.") + return self.oob_predictions_.reshape(-1, 1) + + def apply(self, X): + X = check_array(np.asarray(X, dtype=float)) + return np.column_stack([tree.apply(X) for tree in self.estimators_]) + + def decision_path(self, X): + raise NotImplementedError("decision_path is not yet implemented for the dedicated causal survival trainer.") + + def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): + return np.array(self.feature_importances_, copy=True) + + +# --------------------------------------------------------------------------- +# CausalSurvivalForest +# --------------------------------------------------------------------------- + +class CausalSurvivalForest(BaseEstimator): + """Causal Survival Forest for RMST-based heterogeneous treatment effects. + + Takes raw survival inputs ``(X, T, time, event)`` and internally: + + 1. Estimates propensity ``W_hat = E[T|X]`` with out-of-bag predictions when + available, otherwise explicit held-out predictions. + 2. Fits event survival ``S_hat(t|X,W)`` and censoring survival + ``C_hat(t|X,W)`` using held-out predictions that mirror grf's nuisance + OOB workflow. + 3. Computes the doubly-robust GRF pseudo-outcome ``psi`` via ``_compute_psi`` + (direct port of ``grf::compute_psi``). + 4. Calls a dedicated internal survival trainer on + ``(X, W_centered, psi_numerator / psi_denominator)`` with + ``sample_weight=psi_denominator``. + + This is statistically equivalent to Cui et al. (2023): the GRF causal survival + forest embeds the same doubly-robust moment equation in its splitting criterion. + + Parameters + ---------- + horizon : float or None + RMST time horizon τ. Either ``horizon`` or ``tau`` must be provided. + tau : float or None, default None + Alias for ``horizon`` to match the survival learner APIs and notebooks. + target : {"RMST", "survival.probability"}, default "RMST" + Estimand. "RMST" targets E[min(T,τ)|a=1,X] − E[min(T,τ)|a=0,X]. + "survival.probability" targets P(T>τ|a=1,X) − P(T>τ|a=0,X). + model_t : sklearn regressor or None, default None + Propensity model E[T|X]. Defaults to + ``RegressionForest(n_estimators=max(52, n_estimators // 4))``, + mirroring ``grf::causal_survival_forest`` using a regression forest + for ``W.hat``. + model_cens : grf-style survival estimator or None, default None + Censoring survival model. Defaults to + ``econml.grf.SurvivalForest(num_trees=max(50, min(n_estimators // 4, 500)),`` + ``min_node_size=15)``. + model_event : grf-style survival estimator or None, default None + Event survival model. Defaults to + ``econml.grf.SurvivalForest(num_trees=max(50, min(n_estimators // 4, 500)),`` + ``min_node_size=15)``. + failure_times : array-like or None, default None + Optional event-time grid used for nuisance survival estimation. + sample_fraction : float or None, default None + GRF-style sample fraction. If supplied, this also controls the nuisance + survival forests. + mtry : int or None, default None + Number of candidate split features. If supplied, this is used for both + nuisance survival forests and the final forest. + alpha : float, default 0.05 + GRF-style imbalance control parameter. + imbalance_penalty : float, default 0.0 + Penalty applied to imbalanced split candidates in the nuisance survival + forests and in the final causal-survival forest. + stabilize_splits : bool, default True + Whether treatment and censoring status should contribute to the split + imbalance check in the final causal-survival forest. + fast_logrank : bool, default False + Whether nuisance survival forests should use the approximate fast + log-rank candidate-threshold search. + tune_parameters : {"none", "all"} or iterable, default "none" + Parameters to tune for the default treatment nuisance forest. Mirrors + GRF's current behavior where tuning applies to ``W.hat`` estimation. + tune_num_draws : int, default 32 + Number of random hyperparameter draws scored during default treatment + nuisance tuning. + tune_num_reps : int, default 1 + Number of repeated fits used to average the OOB tuning score for each + random draw. + tune_num_trees : int, default 32 + Number of trees used inside each tuning fit for the default treatment + nuisance. + compute_oob_predictions : bool, default True + Whether nuisance forests and the final forest should cache OOB + predictions on the training sample. + clusters : array-like or None, default None + Optional cluster labels. The nuisance survival forests support + cluster-aware resampling directly; the treatment nuisance and final + forest consume the induced effective sample weights. + equalize_cluster_weights : bool, default False + Whether to reweight clusters to contribute equally. Requires + ``clusters``. + n_estimators : int, default 100 + Number of trees in the causal forest. + criterion : {"mse", "het"}, default "mse" + Splitting criterion — passed to the dedicated final GRF trainer. + max_depth : int or None, default None + min_samples_split : int, default 10 + min_samples_leaf : int, default 5 + min_weight_fraction_leaf : float, default 0.0 + min_var_fraction_leaf : float or None, default None + max_features : "auto" or int, default "auto" + min_impurity_decrease : float, default 0.0 + max_samples : float, default 0.45 + min_balancedness_tol : float, default 0.45 + honest : bool, default True + honesty_fraction : float, default 0.5 + Fraction of each honest subsample used for split selection in the final + forest and the default nuisance survival forests. + honesty_prune_leaves : bool, default True + Whether honest trees should prune leaves without estimation-sample + support. If False, such leaves are skipped at prediction time. + inference : bool, default True + Whether to enable variance / confidence interval estimation. + fit_intercept : bool, default True + subforest_size : int, default 4 + n_jobs : int, default -1 + nuisance_cv : int, default 5 + Backward-compatible fallback used only when the treatment nuisance + model does not expose native OOB predictions. + random_state : int or None, default None + verbose : int, default 0 + + Examples + -------- + >>> import numpy as np + >>> from econml.grf import CausalSurvivalForest + >>> n = 300 + >>> X = np.random.normal(size=(n, 5)) + >>> T = np.random.binomial(1, 0.5, size=n) + >>> time = np.abs(np.random.normal(3, 1, size=n)) + >>> event = np.random.binomial(1, 0.7, size=n) + >>> est = CausalSurvivalForest(horizon=4.0) + >>> est.fit(X, T, time, event) # doctest: +SKIP + >>> est.predict(X[:5]) # doctest: +SKIP + """ + + def __init__(self, horizon=None, *, + tau=None, + target="RMST", + model_t=None, + model_cens=None, + model_event=None, + failure_times=None, + sample_fraction=None, + mtry=None, + alpha=0.05, + imbalance_penalty=0.0, + stabilize_splits=True, + fast_logrank=False, + tune_parameters="none", + tune_num_draws=32, + tune_num_reps=1, + tune_num_trees=32, + compute_oob_predictions=True, + clusters=None, + equalize_cluster_weights=False, + n_estimators=100, + criterion="mse", + max_depth=None, + min_samples_split=10, + min_samples_leaf=5, + min_weight_fraction_leaf=0., + min_var_fraction_leaf=None, + min_var_leaf_on_val=False, + max_features="auto", + min_impurity_decrease=0., + max_samples=.45, + min_balancedness_tol=.45, + honest=True, + honesty_fraction=0.5, + honesty_prune_leaves=True, + inference=True, + fit_intercept=True, + subforest_size=4, + n_jobs=-1, + nuisance_cv=5, + random_state=None, + verbose=0): + if horizon is None and tau is None: + raise TypeError("CausalSurvivalForest requires either `horizon` or `tau`.") + if horizon is not None and tau is not None and not np.isclose(horizon, tau): + raise ValueError("`horizon` and `tau` must match when both are provided.") + + resolved_horizon = float(tau if horizon is None else horizon) + self.horizon = resolved_horizon + self.tau = tau + self.target = target + self.model_t = model_t + self.model_cens = model_cens + self.model_event = model_event + self.failure_times = failure_times + self.sample_fraction = sample_fraction + self.mtry = mtry + self.alpha = alpha + self.imbalance_penalty = imbalance_penalty + self.stabilize_splits = stabilize_splits + self.fast_logrank = fast_logrank + self.tune_parameters = tune_parameters + self.tune_num_draws = tune_num_draws + self.tune_num_reps = tune_num_reps + self.tune_num_trees = tune_num_trees + self.compute_oob_predictions = compute_oob_predictions + self.clusters = clusters + self.equalize_cluster_weights = equalize_cluster_weights + self.nuisance_cv = nuisance_cv + self.n_estimators = n_estimators + self.criterion = criterion + self.max_depth = max_depth + self.min_samples_split = min_samples_split + self.min_samples_leaf = min_samples_leaf + self.min_weight_fraction_leaf = min_weight_fraction_leaf + self.min_var_fraction_leaf = min_var_fraction_leaf + self.min_var_leaf_on_val = min_var_leaf_on_val + self.max_features = mtry if mtry is not None else max_features + self.min_impurity_decrease = min_impurity_decrease + self.max_samples = sample_fraction if sample_fraction is not None else max_samples + self.min_balancedness_tol = 0.5 - alpha + self.honest = honest + self.honesty_fraction = honesty_fraction + self.honesty_prune_leaves = honesty_prune_leaves + self.inference = inference + self.fit_intercept = fit_intercept + self.subforest_size = subforest_size + self.n_jobs = n_jobs + self.random_state = random_state + self.verbose = verbose + + def _validate_grf_args(self): + if not (0 <= self.alpha < 0.5): + raise ValueError("alpha must be in [0, 0.5).") + if self.honest and not (0 < self.honesty_fraction < 1): + raise ValueError("honesty_fraction must be in (0, 1) when honest=True.") + if self.equalize_cluster_weights and self.clusters is None: + raise ValueError("equalize_cluster_weights=True requires clusters.") + if self.tune_parameters == "all": + return + if self.tune_parameters != "none": + allowed = {"sample.fraction", "mtry", "min.node.size", "honesty.fraction", + "honesty.prune.leaves", "alpha", "imbalance.penalty"} + requested = set(np.atleast_1d(self.tune_parameters).tolist()) + if not requested.issubset(allowed): + raise ValueError("Unsupported tune_parameters entries: {}".format(sorted(requested - allowed))) + + def _make_final_trainer(self): + return _CausalSurvivalForestTrainer( + n_estimators=self.n_estimators, + criterion=self.criterion, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + min_weight_fraction_leaf=self.min_weight_fraction_leaf, + min_var_fraction_leaf=self.min_var_fraction_leaf, + min_var_leaf_on_val=self.min_var_leaf_on_val, + max_features=self.max_features, + min_impurity_decrease=self.min_impurity_decrease, + max_samples=self.max_samples, + min_balancedness_tol=self.min_balancedness_tol, + honest=self.honest, + honesty_fraction=self.honesty_fraction, + honesty_prune_leaves=self.honesty_prune_leaves, + inference=self.inference, + fit_intercept=self.fit_intercept, + subforest_size=self.subforest_size, + n_jobs=self.n_jobs, + alpha=self.alpha, + imbalance_penalty=self.imbalance_penalty, + stabilize_splits=self.stabilize_splits, + clusters=self.clusters, + equalize_cluster_weights=self.equalize_cluster_weights, + random_state=self.random_state, + verbose=self.verbose, + ) + + # ------------------------------------------------------------------ + # fit + # ------------------------------------------------------------------ + + def fit(self, X, T, time, event, *, sample_weight=None): + """Fit the causal survival forest. + + Parameters + ---------- + X : array_like, shape (n, d) + Pre-treatment covariates. + T : array_like, shape (n,) + Binary treatment indicator (0 = control, 1 = treated). + time : array_like, shape (n,) + Observed (possibly censored) survival time. Must be non-negative. + event : array_like, shape (n,) + Event indicator: 1 = event occurred, 0 = censored. + sample_weight : array_like, shape (n,) or None + + Returns + ------- + self + """ + X = check_array(np.asarray(X, dtype=float)) + T = np.asarray(T, dtype=float).ravel() + time = np.asarray(time, dtype=float).ravel() + event = np.asarray(event, dtype=float).ravel() + n = X.shape[0] + + if np.any(time < 0): + raise ValueError("Event times must be non-negative.") + if not np.all(np.isin(event, [0, 1])): + raise ValueError("Event indicator must be 0 or 1.") + if event.sum() == 0: + raise ValueError("All observations are censored.") + if not np.all(np.isin(T, [0, 1])): + raise NotImplementedError("CausalSurvivalForest currently supports binary treatment only.") + + self._validate_grf_args() + if self.equalize_cluster_weights and sample_weight is not None: + raise ValueError("sample_weight must be None when equalize_cluster_weights=True.") + _, self.cluster_info_ = _cluster_weight_vector( + self.clusters, self.equalize_cluster_weights, n + ) + base_sample_weight = None if sample_weight is None else np.asarray(sample_weight, dtype=float).ravel() + if base_sample_weight is not None and base_sample_weight.shape[0] != n: + raise ValueError("sample_weight must have the same length as X.") + effective_sample_weight = base_sample_weight + + horizon = self.horizon + target = self.target + if target not in ("RMST", "survival.probability"): + raise ValueError("target must be 'RMST' or 'survival.probability'.") + + # Default nuisance models + model_t = self.model_t + if model_t is None: + if self.tune_parameters == "none": + if self.clusters is None: + model_t = RegressionForest( + n_estimators=max(52, self.n_estimators // 4), + max_features=self.max_features, + max_samples=self.max_samples, + min_balancedness_tol=self.min_balancedness_tol, + random_state=self.random_state, + ) + else: + model_t = _ClusteredTreatmentForest( + n_estimators=max(52, self.n_estimators // 4), + max_features=self.max_features, + sample_fraction=self.max_samples, + min_balancedness_tol=self.min_balancedness_tol, + min_samples_leaf=5, + random_state=self.random_state, + clusters=self.clusters, + equalize_cluster_weights=self.equalize_cluster_weights, + ) + self.tuned_model_t_params_ = None + self.tuning_output_ = None + else: + model_t, tuned, tuning_output = _tune_default_treatment_forest( + X, T, + sample_weight=effective_sample_weight, + n_estimators=max(52, self.n_estimators // 4), + max_features=self.max_features, + max_samples=self.max_samples, + min_balancedness_tol=self.min_balancedness_tol, + random_state=self.random_state, + tune_parameters=self.tune_parameters, + clusters=self.clusters, + tune_num_draws=self.tune_num_draws, + tune_num_reps=self.tune_num_reps, + tune_num_trees=self.tune_num_trees, + equalize_cluster_weights=self.equalize_cluster_weights, + ) + self.tuned_model_t_params_ = tuned + self.tuning_output_ = tuning_output + + model_cens = self.model_cens + if model_cens is None: + model_cens = SurvivalForest( + failure_times=self.failure_times, + num_trees=max(50, min(self.n_estimators // 4, 500)), + sample_fraction=self.max_samples, + mtry=self.max_features if isinstance(self.max_features, int) else None, + min_node_size=15, + honesty=self.honest, + honesty_fraction=self.honesty_fraction, + honesty_prune_leaves=self.honesty_prune_leaves, + alpha=self.alpha, + clusters=self.clusters, + equalize_cluster_weights=self.equalize_cluster_weights, + imbalance_penalty=self.imbalance_penalty, + compute_oob_predictions=self.compute_oob_predictions, + fast_logrank=self.fast_logrank, + num_threads=self.n_jobs, + seed=self.random_state, + max_depth=self.max_depth, + verbose=self.verbose, + ) + + model_event = self.model_event + if model_event is None: + model_event = SurvivalForest( + failure_times=self.failure_times, + num_trees=max(50, min(self.n_estimators // 4, 500)), + sample_fraction=self.max_samples, + mtry=self.max_features if isinstance(self.max_features, int) else None, + min_node_size=15, + honesty=self.honest, + honesty_fraction=self.honesty_fraction, + honesty_prune_leaves=self.honesty_prune_leaves, + alpha=self.alpha, + clusters=self.clusters, + equalize_cluster_weights=self.equalize_cluster_weights, + imbalance_penalty=self.imbalance_penalty, + compute_oob_predictions=self.compute_oob_predictions, + fast_logrank=self.fast_logrank, + num_threads=self.n_jobs, + seed=self.random_state, + max_depth=self.max_depth, + verbose=self.verbose, + ) + + # ---- Modify time/event for RMST (mirror R lines 201-208) ---- + time_mod = time.copy() + event_mod = event.copy() + fY = time_mod.copy() + if target == "RMST": + event_mod[time_mod >= horizon] = 1 + time_mod[time_mod >= horizon] = horizon + fY = time_mod + else: + fY = (time_mod > horizon).astype(float) + + # ---- Time grid (unique observed times) ---- + Y_grid = np.sort(np.unique(time_mod)) if self.failure_times is None else np.sort(np.asarray(self.failure_times, dtype=float).ravel()) + if len(Y_grid) <= 2: + raise ValueError("Number of distinct event times must be > 2.") + if horizon < Y_grid[0]: + raise ValueError("`horizon` cannot be before the first event time.") + + # ---- Propensity W_hat = E[T|X] ---- + model_t_, W_hat = _crossfit_treatment_predictions( + model_t, X, T, sample_weight=effective_sample_weight, + random_state=self.random_state, n_splits=self.nuisance_cv, + clusters=self.clusters + ) + W_centered = T - W_hat + + # ---- Survival nuisance models ---- + XT = np.column_stack([X, T]) + + try: + model_event_ = sk_clone(model_event) + model_event_.set_params(failure_times=Y_grid) + except Exception: + model_event_ = model_event + try: + model_cens_ = sk_clone(model_cens) + model_cens_.set_params(failure_times=Y_grid) + except Exception: + model_cens_ = model_cens + + try: + _fit_survival_nuisance_model( + model_event_, XT, time_mod, event_mod, sample_weight=effective_sample_weight + ) + S_hat = model_event_.oob_predict(XT) if self.compute_oob_predictions else model_event_.predict(XT).predictions + S1_hat = model_event_.predict(np.column_stack([X, np.ones(n)]), failure_times=Y_grid).predictions + S0_hat = model_event_.predict(np.column_stack([X, np.zeros(n)]), failure_times=Y_grid).predictions + except (FloatingPointError, ValueError, np.linalg.LinAlgError, NotImplementedError) as exc: + warnings.warn( + "CausalSurvivalForest event nuisance fit failed; falling back to a constant survival curve. " + f"Original error: {exc}", + RuntimeWarning, + stacklevel=2, + ) + S_hat = np.ones((n, len(Y_grid)), dtype=float) + S1_hat = np.ones((n, len(Y_grid)), dtype=float) + S0_hat = np.ones((n, len(Y_grid)), dtype=float) + model_event_ = None + + try: + _fit_survival_nuisance_model( + model_cens_, XT, time_mod, 1 - event_mod, sample_weight=effective_sample_weight + ) + C_hat = model_cens_.oob_predict(XT) if self.compute_oob_predictions else model_cens_.predict(XT).predictions + except (FloatingPointError, ValueError, np.linalg.LinAlgError, NotImplementedError) as exc: + warnings.warn( + "CausalSurvivalForest censoring nuisance fit failed; falling back to a constant survival curve. " + f"Original error: {exc}", + RuntimeWarning, + stacklevel=2, + ) + C_hat = np.ones((n, len(Y_grid)), dtype=float) + model_cens_ = None + + if target == "RMST": + Y_hat = (W_hat * _expected_survival_rmst(S1_hat, Y_grid) + + (1 - W_hat) * _expected_survival_rmst(S0_hat, Y_grid)) + else: + horizon_idx = np.searchsorted(Y_grid, horizon, side='right') - 1 + horizon_idx = np.clip(horizon_idx, 0, len(Y_grid) - 1) + Y_hat = W_hat * S1_hat[:, horizon_idx] + (1 - W_hat) * S0_hat[:, horizon_idx] + + # ---- Censoring probability at each unit's observed time ---- + if target == "survival.probability": + time_mod2 = time_mod.copy() + event_mod2 = event_mod.copy() + time_mod2[time_mod2 > horizon] = horizon + event_mod2[time_mod > horizon] = 1 + else: + time_mod2, event_mod2 = time_mod, event_mod + + Y_index = np.searchsorted(Y_grid, time_mod2, side='right') - 1 + Y_index = np.clip(Y_index, 0, len(Y_grid) - 1) + C_Y_hat = C_hat[np.arange(n), Y_index] + + if target == "RMST" and np.any(C_Y_hat <= 0.05): + warnings.warn( + "Estimated censoring probabilities go as low as " + f"{C_Y_hat.min():.5f}. Causal survival forest may be unreliable.", + stacklevel=2, + ) + + # ---- Compute GRF pseudo-outcome psi ---- + D_use = event_mod2 if target == "survival.probability" else event_mod + psi_num, psi_denom = _compute_psi( + S_hat, C_hat, C_Y_hat, Y_hat, W_centered, + D_use, fY, Y_index, Y_grid, target, horizon + ) + + # Normalise: pass psi_num/psi_denom as y; psi_denom as sample_weight + # (mirrors GRF's numerator/denominator weighting) + denom_safe = np.where(psi_denom > 0, psi_denom, 1e-10) + pseudo_y = psi_num / denom_safe + + sw = psi_denom if effective_sample_weight is None else psi_denom * effective_sample_weight + + # Store nuisance estimates for diagnostics + self.W_hat_ = W_hat + self.Y_hat_ = Y_hat + self.S_hat_ = S_hat + self.C_hat_ = C_hat + self.S1_hat_ = S1_hat + self.S0_hat_ = S0_hat + self.model_t_nuisance_ = model_t_ + self.model_event_nuisance_ = model_event_ + self.model_cens_nuisance_ = model_cens_ + self.psi_numerator_ = psi_num + self.psi_denominator_ = psi_denom + self.effective_sample_weight_ = effective_sample_weight + self.X_train_ = np.array(X, copy=True) + + # ---- Fit dedicated causal-survival trainer on pseudo-outcome ---- + trainer = self._make_final_trainer() + trainer.fit(X, W_centered.reshape(-1, 1), pseudo_y.reshape(-1, 1), + sample_weight=sw, treatment_raw=T, censor=D_use) + self.final_trainer_ = trainer + self.estimators_ = trainer.estimators_ + return self + + def predict(self, X, interval=False, alpha=0.05): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.predict(X, interval=interval, alpha=alpha) + + def oob_predict(self, Xtrain): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.oob_predict(Xtrain) + + def predict_and_var(self, X): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.predict_and_var(X) + + def predict_var(self, X): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.predict_var(X) + + def predict_projection_and_var(self, X, projector): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.predict_projection_and_var(X, projector) + + def predict_projection(self, X, projector): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.predict_projection(X, projector) + + def predict_projection_var(self, X, projector): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.predict_projection_var(X, projector) + + def apply(self, X): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.apply(X) + + def decision_path(self, X): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.decision_path(X) + + def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.feature_importances( + max_depth=max_depth, depth_decay_exponent=depth_decay_exponent + ) + + @property + def feature_importances_(self): + check_is_fitted(self, "final_trainer_") + return self.final_trainer_.feature_importances_ + + def effect(self, X=None): + """Return treatment-effect predictions. + + When called on the training covariates, return out-of-bag predictions + to mirror grf's training-sample prediction behavior. + """ + if X is None: + X = self.X_train_ + X_arr = check_array(np.asarray(X, dtype=float)) + if hasattr(self, 'X_train_') and X_arr.shape == self.X_train_.shape and np.array_equal(X_arr, self.X_train_): + try: + return np.asarray(self.oob_predict(self.X_train_)).ravel() + except Exception: + pass + return np.asarray(self.predict(X_arr)).ravel() diff --git a/econml/grf/_survival_forest.py b/econml/grf/_survival_forest.py new file mode 100644 index 000000000..23d97c59f --- /dev/null +++ b/econml/grf/_survival_forest.py @@ -0,0 +1,520 @@ +"""GRF-style survival forest wrapper. + +This module provides a light-weight Python analogue of +``grf::survival_forest`` for the censored-outcome workstream. It is not yet a +line-by-line port of GRF's C++ survival trainer, but it mirrors the API shape, +implements honest log-rank splitting in Python, and provides native out-of-bag +survival predictions on the training sample so downstream estimators can follow +the same nuisance-estimation pattern as ``grf::causal_survival_forest``. +""" + +from dataclasses import dataclass +import numpy as np +from sklearn.base import BaseEstimator +from sklearn.utils import check_array +from sklearn.utils.validation import _check_sample_weight + + +@dataclass +class SurvivalForestPredictionResult: + predictions: np.ndarray + failure_times: np.ndarray + + +@dataclass +class _SurvivalTreeNode: + is_leaf: bool + feature: int = -1 + threshold: float = 0.0 + left: object = None + right: object = None + survival: np.ndarray = None + + +def _default_mtry(n_features): + return min(int(np.ceil(np.sqrt(n_features) + 20)), n_features) + + +def _cluster_weight_vector(clusters, equalize_cluster_weights, n): + if clusters is None: + return np.ones(n, dtype=float), None + + clusters = np.asarray(clusters) + if clusters.shape[0] != n: + raise ValueError("clusters must have the same length as X.") + + unique, inverse, counts = np.unique(clusters, return_inverse=True, return_counts=True) + if equalize_cluster_weights: + weights = 1.0 / counts[inverse] + else: + weights = np.ones(n, dtype=float) + return weights, (unique, inverse, counts) + + +def _event_grid_counts(time, event, grid): + idx = np.searchsorted(grid, time, side="right") - 1 + idx = np.clip(idx, 0, len(grid) - 1) + failures = np.bincount(idx[event == 1], minlength=len(grid)).astype(float) + at_risk = np.array([(time >= t).sum() for t in grid], dtype=float) + return failures, at_risk + + +def _leaf_survival_curve(time, event, grid, prediction_type): + if time.size == 0: + return None + failures, at_risk = _event_grid_counts(time, event, grid) + safe_risk = np.maximum(at_risk, 1.0) + hazard = failures / safe_risk + if prediction_type == "Kaplan-Meier": + surv = np.cumprod(1.0 - hazard) + else: + surv = np.exp(-np.cumsum(hazard)) + return np.clip(surv, 1e-3, 1.0) + + +def _logrank_statistic(time, event, left_mask): + if left_mask.sum() == 0 or left_mask.sum() == len(left_mask): + return -np.inf + event_times = np.unique(time[event == 1]) + if event_times.size == 0: + return -np.inf + U = 0.0 + V = 0.0 + left_mask = left_mask.astype(bool) + for t in event_times: + at_risk = time >= t + y = np.sum(at_risk) + if y <= 1: + continue + y1 = np.sum(at_risk & left_mask) + d = np.sum((time == t) & (event == 1)) + d1 = np.sum((time == t) & (event == 1) & left_mask) + expected = y1 * d / y + U += d1 - expected + V += (y1 * (y - y1) * d * (y - d)) / (y * y * (y - 1)) + if V <= 0: + return -np.inf + return (U * U) / V + + +def _candidate_thresholds(values, *, fast_logrank): + uniq = np.unique(values) + if uniq.size <= 1: + return np.array([], dtype=float) + mids = (uniq[:-1] + uniq[1:]) / 2.0 + if not fast_logrank or mids.size <= 32: + return mids + qs = np.linspace(0.05, 0.95, 16) + return np.unique(np.quantile(mids, qs)) + + +class _GRFSurvivalTree: + def __init__(self, *, mtry, min_node_size, max_depth, alpha, + honesty, honesty_fraction, honesty_prune_leaves, + imbalance_penalty, fast_logrank, prediction_type, random_state): + self.mtry = mtry + self.min_node_size = min_node_size + self.max_depth = max_depth + self.alpha = alpha + self.honesty = honesty + self.honesty_fraction = honesty_fraction + self.honesty_prune_leaves = honesty_prune_leaves + self.imbalance_penalty = imbalance_penalty + self.fast_logrank = fast_logrank + self.prediction_type = prediction_type + self.random_state = random_state + + def fit(self, X, Y, D, failure_times): + n = X.shape[0] + if self.honesty: + split_n = int(np.floor(n * self.honesty_fraction)) + est_n = n - split_n + if split_n <= 0 or est_n <= 0: + raise ValueError("honesty_fraction leaves no split or estimation sample.") + perm = self.random_state.permutation(n) + split_idx = perm[:split_n] + est_idx = perm[split_n:] + else: + split_idx = np.arange(n) + est_idx = np.arange(n) + + self.failure_times_ = failure_times + self.root_ = self._grow( + X, Y, D, split_idx, est_idx, depth=0 + ) + return self + + def _grow(self, X, Y, D, split_idx, est_idx, depth): + if split_idx.size == 0: + return _SurvivalTreeNode( + is_leaf=True, + survival=_leaf_survival_curve(Y[est_idx], D[est_idx], self.failure_times_, self.prediction_type) + ) + if (self.max_depth is not None and depth >= self.max_depth) or split_idx.size < 2 * self.min_node_size: + return _SurvivalTreeNode( + is_leaf=True, + survival=_leaf_survival_curve(Y[est_idx], D[est_idx], self.failure_times_, self.prediction_type) + ) + + feature, threshold = self._best_split(X[split_idx], Y[split_idx], D[split_idx]) + if feature is None: + return _SurvivalTreeNode( + is_leaf=True, + survival=_leaf_survival_curve(Y[est_idx], D[est_idx], self.failure_times_, self.prediction_type) + ) + + split_left = split_idx[X[split_idx, feature] <= threshold] + split_right = split_idx[X[split_idx, feature] > threshold] + est_left = est_idx[X[est_idx, feature] <= threshold] + est_right = est_idx[X[est_idx, feature] > threshold] + + if self.honesty and self.honesty_prune_leaves and (est_left.size == 0 or est_right.size == 0): + return _SurvivalTreeNode( + is_leaf=True, + survival=_leaf_survival_curve(Y[est_idx], D[est_idx], self.failure_times_, self.prediction_type) + ) + + left = self._grow(X, Y, D, split_left, est_left, depth + 1) + right = self._grow(X, Y, D, split_right, est_right, depth + 1) + return _SurvivalTreeNode( + is_leaf=False, feature=feature, threshold=float(threshold), left=left, right=right + ) + + def _best_split(self, X, Y, D): + n, p = X.shape + features = self.random_state.choice(p, size=min(self.mtry, p), replace=False) + min_failures = max(1.0, self.alpha * n) + best_score = -np.inf + best_feature = None + best_threshold = None + for feature in features: + thresholds = _candidate_thresholds(X[:, feature], fast_logrank=self.fast_logrank) + for threshold in thresholds: + left = X[:, feature] <= threshold + n_left = np.sum(left) + n_right = n - n_left + if n_left < self.min_node_size or n_right < self.min_node_size: + continue + fail_left = np.sum(D[left] == 1) + fail_right = np.sum(D[~left] == 1) + if fail_left < min_failures or fail_right < min_failures: + continue + score = _logrank_statistic(Y, D, left) + if not np.isfinite(score): + continue + imbalance = abs(n_left - n_right) / n + score = score - self.imbalance_penalty * imbalance + if score > best_score: + best_score = score + best_feature = feature + best_threshold = threshold + return best_feature, best_threshold + + def _predict_one(self, x, node): + while not node.is_leaf: + node = node.left if x[node.feature] <= node.threshold else node.right + return node.survival + + def predict_survival_matrix(self, X): + preds = np.empty((X.shape[0], len(self.failure_times_)), dtype=float) + for i, x in enumerate(X): + surv = self._predict_one(x, self.root_) + if surv is None: + preds[i] = np.nan + else: + preds[i] = surv + return preds + + +class SurvivalForest(BaseEstimator): + """Approximate Python translation of ``grf::survival_forest``. + + The implementation uses an explicit ensemble of honest survival trees with + log-rank-based splitting so it can expose native out-of-bag predictions on + the training sample. + """ + + def __init__(self, *, + failure_times=None, + num_trees=1000, + sample_weights=None, + clusters=None, + equalize_cluster_weights=False, + sample_fraction=0.5, + mtry=None, + min_node_size=15, + honesty=True, + honesty_fraction=0.5, + honesty_prune_leaves=True, + alpha=0.05, + imbalance_penalty=0.0, + prediction_type="Nelson-Aalen", + compute_oob_predictions=True, + fast_logrank=False, + num_threads=None, + seed=None, + tune_parameters="none", + max_depth=None, + verbose=0): + self.failure_times = failure_times + self.num_trees = num_trees + self.sample_weights = sample_weights + self.clusters = clusters + self.equalize_cluster_weights = equalize_cluster_weights + self.sample_fraction = sample_fraction + self.mtry = mtry + self.min_node_size = min_node_size + self.honesty = honesty + self.honesty_fraction = honesty_fraction + self.honesty_prune_leaves = honesty_prune_leaves + self.alpha = alpha + self.imbalance_penalty = imbalance_penalty + self.prediction_type = prediction_type + self.compute_oob_predictions = compute_oob_predictions + self.fast_logrank = fast_logrank + self.num_threads = num_threads + self.seed = seed + self.tune_parameters = tune_parameters + self.max_depth = max_depth + self.verbose = verbose + + def _validate_supported_args(self): + if self.prediction_type not in {"Nelson-Aalen", "Kaplan-Meier"}: + raise ValueError("prediction_type must be 'Kaplan-Meier' or 'Nelson-Aalen'.") + if not (0 < self.sample_fraction <= 1): + raise ValueError("sample_fraction must be in (0, 1].") + if not (0 <= self.alpha < 0.5): + raise ValueError("alpha must be in [0, 0.5).") + if self.honesty and not (0 < self.honesty_fraction < 1): + raise ValueError("honesty_fraction must be in (0, 1) when honesty=True.") + if self.equalize_cluster_weights and self.sample_weights is not None: + raise ValueError("sample_weights must be None when equalize_cluster_weights=True.") + if self.equalize_cluster_weights and self.clusters is None: + raise ValueError("equalize_cluster_weights=True requires clusters.") + def _make_tree(self, random_state): + return _GRFSurvivalTree( + mtry=self.mtry_, + min_node_size=self.min_node_size, + max_depth=self.max_depth, + alpha=self.alpha, + honesty=self.honesty, + honesty_fraction=self.honesty_fraction, + honesty_prune_leaves=self.honesty_prune_leaves, + imbalance_penalty=self.imbalance_penalty, + fast_logrank=self.fast_logrank, + prediction_type=self.prediction_type, + random_state=np.random.RandomState(random_state), + ) + + def _bootstrap_indices(self, n, rng): + if self.cluster_info_ is None: + n_boot = max(1, int(np.ceil(self.sample_fraction * n))) + n_boot = min(n_boot, n) + sample_idx = rng.choice(n, size=n_boot, replace=False) + unsampled = np.setdiff1d(np.arange(n), sample_idx, assume_unique=True) + return sample_idx, unsampled + + unique_clusters, inverse, counts = self.cluster_info_ + n_clusters = len(unique_clusters) + n_boot_clusters = max(1, int(np.ceil(self.sample_fraction * n_clusters))) + n_boot_clusters = min(n_boot_clusters, n_clusters) + sampled_cluster_pos = rng.choice(n_clusters, size=n_boot_clusters, replace=False) + sampled_pos_set = set(sampled_cluster_pos.tolist()) + + if self.equalize_cluster_weights: + k = int(np.min(counts)) + pieces = [] + for pos in sampled_cluster_pos: + members = np.flatnonzero(inverse == pos) + pieces.append(rng.choice(members, size=k, replace=False)) + sample_idx = np.concatenate(pieces) + else: + pieces = [np.flatnonzero(inverse == pos) for pos in sampled_cluster_pos] + sample_idx = np.concatenate(pieces) + + unsampled_cluster_mask = np.array([idx not in sampled_pos_set for idx in inverse]) + unsampled = np.flatnonzero(unsampled_cluster_mask) + return sample_idx, unsampled + + def _predict_survival_matrix_from_trees(self, estimators, X, grid): + preds = np.zeros((X.shape[0], len(grid)), dtype=float) + counts = np.zeros(X.shape[0], dtype=float) + for tree in estimators: + tree_pred = tree.predict_survival_matrix(X) + mask = np.all(np.isfinite(tree_pred), axis=1) + if np.any(mask): + preds[mask] += tree_pred[mask] + counts[mask] += 1 + counts = np.maximum(counts, 1.0) + preds /= counts[:, None] + return np.clip(preds, 1e-3, 1.0) + + def fit(self, X, Y, D, *, sample_weight=None): + self._validate_supported_args() + X = check_array(np.asarray(X, dtype=float)) + Y = np.asarray(Y, dtype=float).ravel() + D = np.asarray(D, dtype=float).ravel() + if X.shape[0] != Y.shape[0] or X.shape[0] != D.shape[0]: + raise ValueError("X, Y and D must have the same number of rows.") + if np.any(Y < 0): + raise ValueError("Event times must be non-negative.") + if not np.all(np.isin(D, [0, 1])): + raise ValueError("D must contain only 0/1 values.") + + n = X.shape[0] + self.mtry_ = self.mtry if self.mtry is not None else _default_mtry(X.shape[1]) + self.X_train_ = np.array(X, copy=True) + self.Y_train_ = np.array(Y, copy=True) + self.D_train_ = np.array(D, copy=True) + base_sample_weight = self.sample_weights if sample_weight is None else sample_weight + self.sample_weight_ = None if base_sample_weight is None else _check_sample_weight(base_sample_weight, X, dtype=float) + cluster_weights, self.cluster_info_ = _cluster_weight_vector( + self.clusters, self.equalize_cluster_weights, n + ) + if self.sample_weight_ is None: + self.effective_sample_weight_ = cluster_weights + else: + self.effective_sample_weight_ = self.sample_weight_ * cluster_weights + + if self.failure_times is None: + self.failure_times_ = np.sort(np.unique(Y)) + else: + grid = np.asarray(self.failure_times, dtype=float).ravel() + if grid.ndim != 1 or grid.size == 0: + raise ValueError("failure_times must be a non-empty 1D array.") + if np.min(Y) < np.min(grid): + raise ValueError("failure_times should start on or before min(Y).") + self.failure_times_ = np.sort(grid) + + rng = np.random.RandomState(self.seed) + self.estimators_ = [] + self.bootstrap_indices_ = [] + + if self.compute_oob_predictions: + oob_sum = np.zeros((n, len(self.failure_times_)), dtype=float) + oob_count = np.zeros(n, dtype=int) + + for _ in range(self.num_trees): + sample_idx, unsampled = self._bootstrap_indices(n, rng) + tree = self._make_tree(rng.randint(np.iinfo(np.int32).max)) + tree.fit( + X[sample_idx], + Y[sample_idx], + D[sample_idx], + self.failure_times_, + ) + self.estimators_.append(tree) + self.bootstrap_indices_.append(sample_idx) + + if self.compute_oob_predictions and unsampled.size > 0: + preds = tree.predict_survival_matrix(X[unsampled]) + valid = np.all(np.isfinite(preds), axis=1) + if np.any(valid): + oob_sum[unsampled[valid]] += preds[valid] + oob_count[unsampled[valid]] += 1 + + if self.compute_oob_predictions: + self.oob_predictions_ = np.empty_like(oob_sum) + mask = oob_count > 0 + if np.any(mask): + self.oob_predictions_[mask] = oob_sum[mask] / oob_count[mask][:, None] + if np.any(~mask): + warn( + "Some inputs do not have OOB survival estimates. Falling back to full-forest predictions.", + UserWarning, + stacklevel=2, + ) + self.oob_predictions_[~mask] = self._predict_survival_matrix_from_trees( + self.estimators_, X[~mask], self.failure_times_ + ) + self.oob_predictions_ = np.clip(self.oob_predictions_, 1e-3, 1.0) + self.predictions_ = self.oob_predictions_ + else: + self.oob_predictions_ = None + self.predictions_ = self._predict_survival_matrix_from_trees( + self.estimators_, X, self.failure_times_ + ) + return self + + def predict_survival_matrix(self, X, *, failure_times=None): + X = check_array(np.asarray(X, dtype=float)) + grid = self.failure_times_ if failure_times is None else np.asarray(failure_times, dtype=float).ravel() + if ( + self.compute_oob_predictions + and failure_times is None + and X.shape == self.X_train_.shape + and np.array_equal(X, self.X_train_) + ): + return np.array(self.oob_predictions_, copy=True) + return self._predict_survival_matrix_from_trees(self.estimators_, X, grid) + + def predict_cumulative_hazard_matrix(self, X, *, failure_times=None): + surv = self.predict_survival_matrix(X, failure_times=failure_times) + return -np.log(np.clip(surv, 1e-8, 1.0)) + + def oob_predict(self, Xtrain=None): + if not self.compute_oob_predictions: + raise ValueError("compute_oob_predictions=False, so no OOB predictions are stored.") + if Xtrain is not None: + Xtrain = check_array(np.asarray(Xtrain, dtype=float)) + if Xtrain.shape != self.X_train_.shape or not np.array_equal(Xtrain, self.X_train_): + raise ValueError("oob_predict is only defined on the training sample.") + return np.array(self.oob_predictions_, copy=True) + + def predict(self, X=None, *, failure_times=None): + if X is None: + preds = self.oob_predict() + grid = self.failure_times_ + else: + preds = self.predict_survival_matrix(X, failure_times=failure_times) + grid = self.failure_times_ if failure_times is None else np.asarray(failure_times, dtype=float).ravel() + return SurvivalForestPredictionResult(predictions=preds, failure_times=np.array(grid, copy=True)) + + +def survival_forest(X, Y, D, *, + failure_times=None, + num_trees=1000, + sample_weights=None, + clusters=None, + equalize_cluster_weights=False, + sample_fraction=0.5, + mtry=None, + min_node_size=15, + honesty=True, + honesty_fraction=0.5, + honesty_prune_leaves=True, + alpha=0.05, + imbalance_penalty=0.0, + prediction_type="Nelson-Aalen", + compute_oob_predictions=True, + fast_logrank=False, + num_threads=None, + seed=None, + tune_parameters="none", + max_depth=None, + verbose=0): + """GRF-style functional entry point mirroring ``grf::survival_forest``.""" + est = SurvivalForest( + failure_times=failure_times, + num_trees=num_trees, + sample_weights=sample_weights, + clusters=clusters, + equalize_cluster_weights=equalize_cluster_weights, + sample_fraction=sample_fraction, + mtry=mtry, + min_node_size=min_node_size, + honesty=honesty, + honesty_fraction=honesty_fraction, + honesty_prune_leaves=honesty_prune_leaves, + alpha=alpha, + imbalance_penalty=imbalance_penalty, + prediction_type=prediction_type, + compute_oob_predictions=compute_oob_predictions, + fast_logrank=fast_logrank, + num_threads=num_threads, + seed=seed, + tune_parameters=tune_parameters, + max_depth=max_depth, + verbose=verbose, + ) + return est.fit(X, Y, D, sample_weight=sample_weights) diff --git a/econml/grf/_translated_causal_forest.py b/econml/grf/_translated_causal_forest.py new file mode 100644 index 000000000..5904f3505 --- /dev/null +++ b/econml/grf/_translated_causal_forest.py @@ -0,0 +1,312 @@ +"""GRF-style causal forest wrapper. + +This module provides a high-level wrapper around EconML's low-level +``econml.grf.classes.CausalForest`` so it can be used in a way that mirrors +``grf::causal_forest(X, Y, W, ...)`` from the R package. + +The existing ``econml.grf.CausalForest`` class is intentionally left unchanged +because it is the residualized final-stage forest used by ``CausalForestDML``. +""" + +from dataclasses import dataclass + +import numpy as np +from sklearn.base import BaseEstimator, clone +from sklearn.model_selection import KFold +from sklearn.utils import check_array + +from .classes import CausalForest as _LowLevelCausalForest, RegressionForest + + +@dataclass +class GRFPredictionResult: + """Container mirroring the shape of ``grf::predict(... )`` output.""" + + predictions: np.ndarray + variance_estimates: np.ndarray | None = None + lower_bound: np.ndarray | None = None + upper_bound: np.ndarray | None = None + debiased_error: np.ndarray | None = None + excess_error: np.ndarray | None = None + + +def _default_mtry(n_features): + return min(int(np.ceil(np.sqrt(n_features) + 20)), n_features) + + +def _validate_nuisance(name, value, n): + if value is None: + return None + arr = np.asarray(value, dtype=float).ravel() + if arr.size == 1: + return np.repeat(arr.item(), n) + if arr.size != n: + raise ValueError(f"{name} has incorrect length.") + return arr + + +def _same_matrix(X, X_ref): + return X_ref is not None and X.shape == X_ref.shape and np.array_equal(X, X_ref) + + +def _diag_or_ravel(var): + var = np.asarray(var) + if var.ndim == 3: + return np.diagonal(var, axis1=1, axis2=2).reshape(var.shape[0], -1)[:, 0] + return var.ravel() + + +class GRFCausalForest(BaseEstimator): + """Python translation of ``grf::causal_forest`` built on EconML GRF primitives. + + Parameters mirror the core R API where possible. Unsupported GRF features + are accepted only at their default values and otherwise raise. + """ + + def __init__(self, *, + model_y=None, + model_t=None, + num_trees=2000, + sample_fraction=0.5, + mtry=None, + min_node_size=5, + honesty=True, + honesty_fraction=0.5, + honesty_prune_leaves=True, + alpha=0.05, + imbalance_penalty=0.0, + stabilize_splits=True, + ci_group_size=2, + compute_oob_predictions=True, + num_threads=None, + seed=None, + clusters=None, + equalize_cluster_weights=False, + tune_parameters="none", + tune_num_trees=200, + tune_num_reps=50, + tune_num_draws=1000, + max_depth=None, + min_impurity_decrease=0.0, + min_var_fraction_leaf=None, + min_var_leaf_on_val=False, + inference=True, + fit_intercept=True, + verbose=0): + self.model_y = model_y + self.model_t = model_t + self.num_trees = num_trees + self.sample_fraction = sample_fraction + self.mtry = mtry + self.min_node_size = min_node_size + self.honesty = honesty + self.honesty_fraction = honesty_fraction + self.honesty_prune_leaves = honesty_prune_leaves + self.alpha = alpha + self.imbalance_penalty = imbalance_penalty + self.stabilize_splits = stabilize_splits + self.ci_group_size = ci_group_size + self.compute_oob_predictions = compute_oob_predictions + self.num_threads = num_threads + self.seed = seed + self.clusters = clusters + self.equalize_cluster_weights = equalize_cluster_weights + self.tune_parameters = tune_parameters + self.tune_num_trees = tune_num_trees + self.tune_num_reps = tune_num_reps + self.tune_num_draws = tune_num_draws + self.max_depth = max_depth + self.min_impurity_decrease = min_impurity_decrease + self.min_var_fraction_leaf = min_var_fraction_leaf + self.min_var_leaf_on_val = min_var_leaf_on_val + self.inference = inference + self.fit_intercept = fit_intercept + self.verbose = verbose + + def _validate_supported_args(self): + if self.clusters is not None: + raise NotImplementedError("clusters are not yet supported in GRFCausalForest.") + if self.equalize_cluster_weights: + raise NotImplementedError("equalize_cluster_weights is not yet supported in GRFCausalForest.") + if self.tune_parameters != "none": + raise NotImplementedError("tune_parameters is not yet supported in GRFCausalForest.") + if self.honesty_fraction != 0.5: + raise NotImplementedError("honesty_fraction values other than 0.5 are not yet supported.") + if not self.honesty_prune_leaves: + raise NotImplementedError("honesty_prune_leaves=False is not yet supported.") + if self.imbalance_penalty != 0.0: + raise NotImplementedError("imbalance_penalty is not yet supported in GRFCausalForest.") + if not self.stabilize_splits: + raise NotImplementedError("stabilize_splits=False is not yet supported in GRFCausalForest.") + + def _make_default_nuisance_forest(self, n_features): + return RegressionForest( + n_estimators=max(50, self.num_trees // 4), + max_depth=self.max_depth, + min_samples_split=max(2 * self.min_node_size, 2), + min_samples_leaf=self.min_node_size, + max_features=self.mtry_ if self.mtry_ is not None else _default_mtry(n_features), + min_impurity_decrease=self.min_impurity_decrease, + max_samples=self.sample_fraction, + honest=True, + inference=False, + n_jobs=-1 if self.num_threads is None else self.num_threads, + random_state=self.seed, + verbose=self.verbose, + ) + + def _crossfit_regression(self, model, X, y, sample_weight): + fitted = clone(model) + fitted.fit(X, y, sample_weight=sample_weight) + + preds = None + if hasattr(fitted, "oob_predict"): + try: + preds = np.asarray(fitted.oob_predict(X)).ravel() + except Exception: + preds = None + + if preds is None or np.any(~np.isfinite(preds)): + splitter = KFold(n_splits=2, shuffle=True, random_state=self.seed) + preds = np.empty(X.shape[0], dtype=float) + for train_idx, test_idx in splitter.split(X): + fold_model = clone(model) + fold_sw = None if sample_weight is None else sample_weight[train_idx] + fold_model.fit(X[train_idx], y[train_idx], sample_weight=fold_sw) + preds[test_idx] = np.asarray(fold_model.predict(X[test_idx])).ravel() + return fitted, preds + + def _crossfit_final_predictions(self, X): + splitter = KFold(n_splits=2, shuffle=True, random_state=self.seed) + preds = np.empty(X.shape[0], dtype=float) + for train_idx, test_idx in splitter.split(X): + fold_forest = self._make_final_forest() + fold_sw = None if self.sample_weight_ is None else self.sample_weight_[train_idx] + fold_forest.fit( + self.X_train_[train_idx], + self.W_centered_[train_idx], + self.Y_centered_[train_idx], + sample_weight=fold_sw, + ) + preds[test_idx] = np.asarray(fold_forest.predict(self.X_train_[test_idx])).ravel() + return preds + + def _make_final_forest(self): + return _LowLevelCausalForest( + n_estimators=self.num_trees, + criterion="mse", + max_depth=self.max_depth, + min_samples_split=max(2 * self.min_node_size, 2), + min_samples_leaf=self.min_node_size, + min_var_fraction_leaf=self.min_var_fraction_leaf, + min_var_leaf_on_val=self.min_var_leaf_on_val, + max_features=self.mtry_, + min_impurity_decrease=self.min_impurity_decrease, + max_samples=self.sample_fraction, + min_balancedness_tol=0.5 - self.alpha, + honest=self.honesty, + inference=self.inference, + fit_intercept=self.fit_intercept, + subforest_size=self.ci_group_size, + n_jobs=-1 if self.num_threads is None else self.num_threads, + random_state=self.seed, + verbose=self.verbose, + ) + + def fit(self, X, Y, W, *, sample_weight=None, Y_hat=None, W_hat=None): + self._validate_supported_args() + X = check_array(np.asarray(X, dtype=float)) + Y = np.asarray(Y, dtype=float).ravel() + W = np.asarray(W, dtype=float).ravel() + if X.shape[0] != Y.shape[0] or X.shape[0] != W.shape[0]: + raise ValueError("X, Y and W must have the same number of rows.") + if not (0 <= self.alpha < 0.5): + raise ValueError("alpha must be in [0, 0.5).") + + self.mtry_ = self.mtry if self.mtry is not None else _default_mtry(X.shape[1]) + self.X_train_ = np.array(X, copy=True) + self.Y_train_ = np.array(Y, copy=True) + self.W_train_ = np.array(W, copy=True) + self.sample_weight_ = None if sample_weight is None else np.asarray(sample_weight, dtype=float).ravel() + + Y_hat = _validate_nuisance("Y_hat", Y_hat, X.shape[0]) + W_hat = _validate_nuisance("W_hat", W_hat, X.shape[0]) + + if Y_hat is None: + base_model_y = self.model_y if self.model_y is not None else self._make_default_nuisance_forest(X.shape[1]) + self.model_y_nuisance_, Y_hat = self._crossfit_regression(base_model_y, X, Y, self.sample_weight_) + else: + self.model_y_nuisance_ = None + + if W_hat is None: + base_model_t = self.model_t if self.model_t is not None else self._make_default_nuisance_forest(X.shape[1]) + self.model_t_nuisance_, W_hat = self._crossfit_regression(base_model_t, X, W, self.sample_weight_) + else: + self.model_t_nuisance_ = None + + self.Y_hat_ = Y_hat + self.W_hat_ = W_hat + self.Y_centered_ = Y - Y_hat + self.W_centered_ = (W - W_hat).reshape(-1, 1) + + self.forest_ = self._make_final_forest() + self.forest_.fit(X, self.W_centered_, self.Y_centered_, sample_weight=self.sample_weight_) + + if self.compute_oob_predictions: + try: + self.oob_predictions_ = np.asarray(self.forest_.oob_predict(self.X_train_)).ravel() + if np.any(~np.isfinite(self.oob_predictions_)): + raise ValueError("non-finite oob predictions") + except Exception: + self.oob_predictions_ = self._crossfit_final_predictions(self.X_train_) + else: + self.oob_predictions_ = None + + return self + + def effect(self, X=None): + return self.predict(X).predictions + + def predict(self, X=None, interval=False, alpha=0.05, estimate_variance=False): + if X is None: + if not self.compute_oob_predictions: + raise ValueError("predict(X=None) requires compute_oob_predictions=True.") + if interval or estimate_variance: + raise NotImplementedError("OOB intervals and OOB variance estimates are not implemented.") + return GRFPredictionResult(predictions=np.array(self.oob_predictions_, copy=True)) + + X = check_array(np.asarray(X, dtype=float)) + if _same_matrix(X, self.X_train_) and self.compute_oob_predictions and not interval and not estimate_variance: + return GRFPredictionResult(predictions=np.array(self.oob_predictions_, copy=True)) + + if interval: + pred, lb, ub = self.forest_.predict(X, interval=True, alpha=alpha) + return GRFPredictionResult( + predictions=np.asarray(pred).ravel(), + lower_bound=np.asarray(lb).ravel(), + upper_bound=np.asarray(ub).ravel(), + ) + + if estimate_variance: + pred, var = self.forest_.predict_and_var(X) + return GRFPredictionResult( + predictions=np.asarray(pred).ravel(), + variance_estimates=_diag_or_ravel(var), + ) + + pred = self.forest_.predict(X) + return GRFPredictionResult(predictions=np.asarray(pred).ravel()) + + +def causal_forest(X, Y, W, *, + Y_hat=None, + W_hat=None, + sample_weight=None, + **kwargs): + """Fit and return a GRF-style causal forest. + + This is the closest Python analogue to ``grf::causal_forest(X, Y, W, ...)`` + in this repository. + """ + forest = GRFCausalForest(**kwargs) + return forest.fit(X, Y, W, sample_weight=sample_weight, Y_hat=Y_hat, W_hat=W_hat) diff --git a/econml/metalearners/__init__.py b/econml/metalearners/__init__.py index a12392242..bdf0fe28f 100644 --- a/econml/metalearners/__init__.py +++ b/econml/metalearners/__init__.py @@ -1,9 +1,32 @@ -# Copyright (c) PyWhy contributors. All rights reserved. -# Licensed under the MIT License. - -from ._metalearners import (TLearner, SLearner, XLearner, DomainAdaptationLearner) - -__all__ = ["TLearner", - "SLearner", - "XLearner", - "DomainAdaptationLearner"] +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +from ._metalearners import DomainAdaptationLearner +from ._censor_metalearners import (TLearner, SLearner, XLearner, + SurvivalTLearner, SurvivalSLearner, + CompetingRisksTLearner, CompetingRisksSLearner, + SeparableDirectAstar1TLearner, SeparableIndirectAstar1TLearner, + SeparableDirectAstar1SLearner, SeparableIndirectAstar1SLearner, + IPTWLearner, AIPTWLearner, ULearner, MCLearner, + MCEALearner, RALearner, RLearner, IFLearner) +__all__ = ["TLearner", + "SLearner", + "XLearner", + "DomainAdaptationLearner", + "SurvivalTLearner", + "SurvivalSLearner", + "CompetingRisksTLearner", + "CompetingRisksSLearner", + "SeparableDirectAstar1TLearner", + "SeparableIndirectAstar1TLearner", + "SeparableDirectAstar1SLearner", + "SeparableIndirectAstar1SLearner", + "IPTWLearner", + "AIPTWLearner", + "ULearner", + "MCLearner", + "MCEALearner", + "RALearner", + "RLearner", + "IFLearner", + ] diff --git a/econml/metalearners/_censor_metalearners.py b/econml/metalearners/_censor_metalearners.py new file mode 100644 index 000000000..9246ea5ff --- /dev/null +++ b/econml/metalearners/_censor_metalearners.py @@ -0,0 +1,2012 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +"""Cross-fitted metalearners for heterogeneous treatment effects. + +This module implements OOS-only learners without relying on ``_OrthoLearner``. +The implementation is organized around explicit stages: + +``_crossfit_nuisances`` + Cross-fit nuisance models only. +``_crossfit_final`` / ``_fit_final`` + Cross-fit or fit the learner-specific final model. +``effects`` + Return HTE estimates, using cached OOF values on the training rows. +""" + +import warnings + +import numpy as np +from sklearn.base import BaseEstimator, clone +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.model_selection import KFold, StratifiedKFold, check_cv +from sklearn.utils import check_array +from sksurv.ensemble import RandomSurvivalForest +from sksurv.functions import StepFunction + +from ..censor._nuisance import _make_sksurv_y + + +_PROPENSITY_CLIP = 1e-3 + + +# --------------------------------------------------------------------------- +# Shared helpers +# --------------------------------------------------------------------------- + +def _make_default_binary_nuisance_model(): + return RandomForestClassifier( + n_estimators=100, + min_samples_leaf=5, + random_state=123, + ) + + +def _make_default_continuous_nuisance_model(): + return RandomForestRegressor( + n_estimators=100, + min_samples_leaf=5, + random_state=123, + ) + + +def _make_default_survival_nuisance_model(): + return RandomSurvivalForest( + n_estimators=100, + min_samples_leaf=5, + random_state=123, + ) + +def _resolve_binary_nuisance_model(model): + return _make_default_binary_nuisance_model() if model in ('auto', None) else model + + +def _resolve_continuous_nuisance_model(model): + return _make_default_continuous_nuisance_model() if model in ('auto', None) else model + + +def _resolve_survival_nuisance_model(model): + return _make_default_survival_nuisance_model() if model in ('auto', None) else model + + +def _build_interaction_features(treatment, X): + t = np.asarray(treatment, dtype=float).reshape(-1, 1) + X_arr = np.asarray(X, dtype=float) + return np.hstack([t, X_arr, t * X_arr]) + + +def _eval_survival_on_grid(model, X, time_grid): + surv_fns = model.predict_survival_function(np.asarray(X)) + grid = np.asarray(time_grid, dtype=float) + vals = [] + for fn in surv_fns: + dom = getattr(fn, 'domain', None) + if dom is not None: + grid_eval = np.clip(grid, dom[0], dom[1]) + else: + x = np.asarray(getattr(fn, 'x', grid), dtype=float) + grid_eval = np.clip(grid, x[0], x[-1]) + vals.append(np.clip(fn(grid_eval), 1e-3, 1.0)) + return np.asarray(vals, dtype=float) + + +def _compute_rmst(surv_fns, tau, time_grid=None): + tau = float(tau) + if isinstance(surv_fns, np.ndarray) and surv_fns.dtype != object: + surv_arr = np.asarray(surv_fns, dtype=float) + if time_grid is None: + raise TypeError("_compute_rmst needs time_grid when given a raw survival array.") + grid = np.asarray(time_grid, dtype=float) + if grid.ndim != 1: + raise ValueError("time_grid must be one-dimensional.") + if surv_arr.ndim != 2 or surv_arr.shape[1] != grid.shape[0]: + raise ValueError("Raw survival array must have shape (n, len(time_grid)).") + grid_tau = grid[grid <= tau] + if grid_tau.size == 0 or grid_tau[-1] < tau: + grid_tau = np.append(grid_tau, tau) + vals = np.empty((surv_arr.shape[0], grid_tau.size), dtype=float) + for i in range(surv_arr.shape[0]): + vals[i] = np.interp(grid_tau, grid, surv_arr[i], left=1.0, right=surv_arr[i, -1]) + return np.trapz(vals, grid_tau, axis=1) + out = [] + for fn in surv_fns: + x = np.asarray(fn.x, dtype=float) + y = np.asarray(fn.y, dtype=float) + if x[0] > 0: + x = np.insert(x, 0, 0.0) + y = np.insert(y, 0, 1.0) + x = np.append(x, tau) + x = np.clip(x, 0.0, tau) + x = np.maximum.accumulate(x) + y_ext = np.append(y, y[-1]) + area = 0.0 + for left, right, val in zip(x[:-1], x[1:], y_ext[:-1]): + if right > left: + area += (right - left) * val + out.append(area) + return np.asarray(out, dtype=float) + + +def _compute_cif_on_grid(overall_surv, cause_surv): + overall = np.clip(np.asarray(overall_surv, dtype=float), 1e-3, 1.0) + cause = np.clip(np.asarray(cause_surv, dtype=float), 1e-3, 1.0) + n, m = overall.shape + cif = np.zeros((n, m), dtype=float) + prev_overall = np.ones(n, dtype=float) + prev_cause = np.ones(n, dtype=float) + running = np.zeros(n, dtype=float) + for j in range(m): + hazard = 1.0 - np.clip(cause[:, j] / prev_cause, 0.0, 1.0) + running = running + prev_overall * hazard + cif[:, j] = np.clip(running, 0.0, 1.0) + prev_overall = overall[:, j] + prev_cause = cause[:, j] + return cif + + +def _compute_rmtl_from_cif(cif, time_grid, tau): + cif_arr = np.asarray(cif, dtype=float) + grid = np.asarray(time_grid, dtype=float) + tau = float(tau) + if grid.ndim != 1: + raise ValueError("time_grid must be one-dimensional.") + if grid.size == 0: + return np.zeros(cif_arr.shape[0], dtype=float) + grid_tau = grid[grid <= tau] + if grid_tau.size == 0 or grid_tau[-1] < tau: + grid_tau = np.append(grid_tau, tau) + vals = np.empty((cif_arr.shape[0], grid_tau.size), dtype=float) + for i in range(cif_arr.shape[0]): + vals[i] = np.interp(grid_tau, grid, cif_arr[i], left=0.0, right=cif_arr[i, -1]) + return np.trapz(vals, grid_tau, axis=1) + +def _fit_propensity_fold(model, T, X): + """Fit propensity model and return clipped probabilities.""" + m = clone(model, safe=False) + m.fit(X, T) + if hasattr(m, 'predict_proba'): + e = m.predict_proba(X)[:, 1] + else: + e = m.predict(X) + return np.clip(e, _PROPENSITY_CLIP, 1 - _PROPENSITY_CLIP), m + + +def _fit_mu_fold(model, Y, T, X): + """Fit per-arm outcome models; return (mu_0, mu_1, model_0, model_1).""" + m0 = clone(model, safe=False) + m1 = clone(model, safe=False) + mask0, mask1 = T == 0, T == 1 + m0.fit(X[mask0], Y[mask0]) + m1.fit(X[mask1], Y[mask1]) + mu0 = m0.predict(X) + mu1 = m1.predict(X) + return mu0, mu1, m0, m1 + + +def _same_train_features(X, X_train): + """Whether X matches the training covariates exactly.""" + if X is None or X_train is None: + return False + X_arr = np.asarray(X) + return X_arr.shape == X_train.shape and np.array_equal(X_arr, X_train) + + +def _build_outer_oof_folds(cv, X, T, random_state): + """Build OOF folds for nuisance or final cross-fitting.""" + if cv == 1: + raise ValueError("CrossFit learners require cv >= 2 for OOF prediction caching") + + splitter = check_cv(cv, [0], classifier=True) + if splitter != cv and isinstance(splitter, (KFold, StratifiedKFold)): + splitter.shuffle = True + splitter.random_state = random_state + return list(splitter.split(np.asarray(X), np.asarray(T).ravel().astype(int))) + + +def _as_tuple(values): + return values if isinstance(values, tuple) else (values,) + + +def _subset_tuple(values, idx): + return tuple(np.asarray(v)[idx] for v in values) + + +def _crossfit_nuisances(nuisance_factory, Y, T, *, X, cv, random_state): + """Cross-fit nuisance models and stitch held-out nuisance predictions.""" + X_arr = np.asarray(X) + T_arr = np.asarray(T).ravel().astype(int) + folds = _build_outer_oof_folds(cv, X_arr, T_arr, random_state) + fitted = [] + oof = None + + for train_idx, test_idx in folds: + nuisance = nuisance_factory() + nuisance.fit(Y[train_idx], T_arr[train_idx], X=X_arr[train_idx]) + preds = _as_tuple(nuisance.predict(Y[test_idx], T_arr[test_idx], X=X_arr[test_idx])) + preds = tuple(np.asarray(p) for p in preds) + if oof is None: + oof = [np.full((X_arr.shape[0],) + p.shape[1:], np.nan, dtype=float) for p in preds] + for slot, pred in zip(oof, preds): + slot[test_idx] = pred + fitted.append(nuisance) + + if oof is None: + return (), [] + return tuple(oof), fitted + + +def _fit_final(final_model, Y, T, *, X, nuisances=(), sample_weight=None): + """Fit the final model once on the full sample only.""" + model = clone(final_model, safe=False) + model.fit( + Y, T, X=np.asarray(X), nuisances=nuisances, + sample_weight=sample_weight, + ) + return model + + +def _crossfit_final(final_model, Y, T, *, X, nuisances=(), cv, random_state): + """Cross-fit the final model only and return the full fit plus OOF effects.""" + X_arr = np.asarray(X) + T_arr = np.asarray(T).ravel().astype(int) + folds = _build_outer_oof_folds(cv, X_arr, T_arr, random_state) + full_model = _fit_final(final_model, Y, T_arr, X=X_arr, nuisances=nuisances) + oof = np.full(X_arr.shape[0], np.nan, dtype=float) + + for train_idx, test_idx in folds: + fold_model = _fit_final( + final_model, + Y[train_idx], + T_arr[train_idx], + X=X_arr[train_idx], + nuisances=_subset_tuple(nuisances, train_idx), + ) + oof[test_idx] = np.asarray(fold_model.predict(X_arr[test_idx])).ravel() + + return full_model, oof + + +def _cache_training_oof_effects(estimator, Y, T, *, X): + """Cache an extra outer OOF effect layer on the full training dataset.""" + X_arr = np.asarray(X) + T_arr = np.asarray(T).ravel().astype(int) + oof = np.full(X_arr.shape[0], np.nan, dtype=float) + + for train_idx, test_idx in _build_outer_oof_folds(estimator.cv, X_arr, T_arr, estimator.random_state): + est = clone(estimator, safe=False) + est._skip_training_oof_ = True + est._skip_training_separable_oof_ = True + est.fit(Y[train_idx], T_arr[train_idx], X=X_arr[train_idx]) + oof[test_idx] = np.asarray(est.effect(X_arr[test_idx])).ravel() + + return oof + + +def effects(estimator, X=None): + """Return HTE estimates, using cached OOF values on the training rows.""" + if _same_train_features(X, getattr(estimator, '_training_X_oof_', None)): + return np.asarray(estimator._training_oof_effect_).ravel() + return np.asarray(estimator.const_marginal_effect(X)).ravel() + + +class _BaseCrossfitEstimator(BaseEstimator): + """Minimal estimator API for OOS-only metalearners.""" + + def __init__(self, *, cv=3, categories='auto', random_state=None): + self.cv = cv + self.categories = categories + self.random_state = random_state + self._training_X_oof_ = None + self._training_oof_effect_ = None + self._crossfit_split_stages_ = 0 + self._entire_dataset_caching_split_stages_ = 0 + + def _check_fitted_dims(self, X): + X_arr = np.asarray(X) + n_features = getattr(self, '_n_features_in_', None) + if n_features is not None and X_arr.shape[1] != n_features: + raise ValueError( + f"X has {X_arr.shape[1]} features but this estimator was fit with {n_features}." + ) + + def effect(self, X): + return effects(self, X) + + def effects(self, X): + return self.effect(X) + + def ate(self, X): + return float(np.mean(self.effect(X))) + + +def _cache_training_oof_separable(estimator, Y, T, X): + """Cache outer-fold OOF separable direct/indirect predictions on training rows.""" + X_arr = np.asarray(X) + T_arr = np.asarray(T).ravel().astype(int) + direct = np.full(X_arr.shape[0], np.nan) + indirect = np.full(X_arr.shape[0], np.nan) + + for train_idx, test_idx in _build_outer_oof_folds(estimator.cv, X_arr, T_arr, estimator.random_state): + est = clone(estimator, safe=False) + est._skip_training_oof_ = True + est._skip_training_separable_oof_ = True + est.fit(Y[train_idx], T_arr[train_idx], X=X_arr[train_idx]) + direct_fold, indirect_fold = est._compute_separable_effects(X_arr[test_idx]) + direct[test_idx] = np.asarray(direct_fold).ravel() + indirect[test_idx] = np.asarray(indirect_fold).ravel() + + return direct, indirect + + +class _ConstantSurvivalModel: + """Fallback survival model for fold slices with no observed events.""" + + def __init__(self, max_time): + max_time = float(max(max_time, 1e-8)) + self._fn = StepFunction( + x=np.array([0.0, max_time], dtype=float), + y=np.array([1.0, 1.0], dtype=float), + ) + + def predict_survival_function(self, X): + return [self._fn] * np.asarray(X).shape[0] + + +def _fit_survival_or_constant(model, X, time, event_bool): + """Fit a survival model, falling back to constant survival if needed.""" + if not np.any(event_bool): + return _ConstantSurvivalModel(np.max(time) if len(time) else 1.0) + fitted = clone(model, safe=False) + try: + fitted.fit(X, _make_sksurv_y(time, event_bool)) + return fitted + except (FloatingPointError, ValueError, np.linalg.LinAlgError) as exc: + warnings.warn( + "Survival nuisance model fit failed on a fold; " + "falling back to a constant survival curve. " + f"Original error: {exc}", + RuntimeWarning, + stacklevel=2, + ) + return _ConstantSurvivalModel(np.max(time) if len(time) else 1.0) + + +def _mean_nuisance_predictions(models_nuisance, X): + """Average nuisance predictions across fitted cross-fit models.""" + X_arr = check_array(np.asarray(X, dtype=float)) + accum = None + n_models = 0 + for fitted_per_iter in models_nuisance: + for mdl in fitted_per_iter: + nuisances = mdl.predict(None, None, X=X_arr) + if not isinstance(nuisances, tuple): + nuisances = (nuisances,) + nuisances = tuple(np.asarray(n, dtype=float) for n in nuisances) + if accum is None: + accum = [np.array(n, copy=True) for n in nuisances] + else: + for idx, nuis in enumerate(nuisances): + accum[idx] += nuis + n_models += 1 + if accum is None or n_models == 0: + raise RuntimeError("No fitted nuisance models are available for prediction.") + return tuple(n / n_models for n in accum) + + +class _DirectNuisanceCateMixin: + """Predict CATE directly from averaged nuisance models instead of a final regressor.""" + + def _effect_from_nuisances(self, nuisances): + raise NotImplementedError + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + X_arr = getattr(self, '_training_X_oof_', None) if X is None else X + if X_arr is None: + raise ValueError("X must be provided for direct learner effect prediction.") + nuisances = _mean_nuisance_predictions(self.models_nuisance_, X_arr) + return np.asarray(self._effect_from_nuisances(nuisances), dtype=float).reshape(-1, 1) + + +def _fit_oof_direct(estimator, nuisance_factory, Y, T, *, X, cache_training_oof=False): + """Fit a direct learner whose effect is computed only from nuisances.""" + X_arr = np.asarray(X) + T_arr = np.asarray(T).ravel().astype(int) + estimator._n_features_in_ = X_arr.shape[1] + estimator._training_X_oof_ = np.array(X_arr, copy=True) + estimator._crossfit_split_stages_ = 1 + estimator._entire_dataset_caching_split_stages_ = 1 if cache_training_oof else 0 + nuisances, fitted = _crossfit_nuisances( + nuisance_factory, Y, T_arr, X=X_arr, cv=estimator.cv, random_state=estimator.random_state + ) + estimator.models_nuisance_ = [tuple(fitted)] + base_oof = np.asarray(estimator._effect_from_nuisances(nuisances), dtype=float).ravel() + estimator._training_oof_effect_ = base_oof + if cache_training_oof and not getattr(estimator, '_skip_training_oof_', False): + estimator._training_oof_effect_ = _cache_training_oof_effects( + estimator, Y, T_arr, X=X_arr + ) + return estimator + + +def _fit_oof_two_stage(estimator, nuisance_factory, final_factory, Y, T, *, X, + cache_training_oof=False, caching_stage_count=None): + """Fit a learner with cross-fitted nuisances and a cross-fitted final model.""" + X_arr = np.asarray(X) + T_arr = np.asarray(T).ravel().astype(int) + estimator._n_features_in_ = X_arr.shape[1] + estimator._training_X_oof_ = np.array(X_arr, copy=True) + estimator._crossfit_split_stages_ = 2 + if caching_stage_count is None: + estimator._entire_dataset_caching_split_stages_ = 1 if cache_training_oof else 0 + else: + estimator._entire_dataset_caching_split_stages_ = int(caching_stage_count) + nuisances, fitted = _crossfit_nuisances( + nuisance_factory, Y, T_arr, X=X_arr, cv=estimator.cv, random_state=estimator.random_state + ) + estimator.models_nuisance_ = [tuple(fitted)] + estimator.model_final_, oof = _crossfit_final( + final_factory(), Y, T_arr, X=X_arr, nuisances=nuisances, + cv=estimator.cv, random_state=estimator.random_state, + ) + estimator._training_oof_effect_ = oof + if cache_training_oof and not getattr(estimator, '_skip_training_oof_', False): + estimator._training_oof_effect_ = _cache_training_oof_effects( + estimator, Y, T_arr, X=X_arr + ) + return estimator + + +# --------------------------------------------------------------------------- +# CrossFitTLearner +# --------------------------------------------------------------------------- + +class _TLNuisance: + """Nuisance for CrossFitTLearner: per-arm outcome models mu_0, mu_1.""" + + def __init__(self, model_mu): + self._model_mu = model_mu + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int) + Y_arr = np.asarray(Y).ravel() + X_arr = np.asarray(X) + self._mu0, self._mu1, self._m0, self._m1 = _fit_mu_fold( + self._model_mu, Y_arr, T_arr, X_arr) + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + mu0 = self._m0.predict(X_arr) + mu1 = self._m1.predict(X_arr) + return mu0, mu1 + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class _TLFinal: + """Final stage for CrossFitTLearner: regress (mu_1 - mu_0) on X.""" + + def __init__(self, model_final): + self._model_final = clone(model_final, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + mu0, mu1 = nuisances + target = np.asarray(mu1 - mu0, dtype=float).ravel() + mask = np.isfinite(target) + if not np.any(mask): + raise ValueError("Final TLearner target contains no finite entries.") + self._model_final.fit(np.asarray(X)[mask], target[mask]) + return self + + def predict(self, X=None): + return self._model_final.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + mu0, mu1 = nuisances + target = np.asarray(mu1 - mu0, dtype=float).ravel() + mask = np.isfinite(target) + pred = self._model_final.predict(np.asarray(X)[mask]) + return np.mean((target[mask] - pred) ** 2) + + +class TLearner(_DirectNuisanceCateMixin, _BaseCrossfitEstimator): + """T-learner with cross-fitting. + + Fits per-arm outcome models mu_0(X) and mu_1(X) on held-out folds and + predicts CATE directly as mu_1(X) - mu_0(X). Cross-fitting via ``cv`` + folds yields fold-held-out training predictions. + + Parameters + ---------- + model_mu : sklearn regressor + Outcome model fitted separately for each treatment arm. + cv : int, default 3 + Number of cross-fitting folds. + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, model_mu='auto', cv=3, + categories='auto', random_state=None): + self.model_mu = _resolve_continuous_nuisance_model(model_mu) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def _effect_from_nuisances(self, nuisances): + mu0, mu1 = nuisances + return mu1 - mu0 + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitTLearner. + + Parameters + ---------- + Y : array_like, shape (n,) + Outcome or pre-computed pseudo-outcome. + T : array_like, shape (n,) + Binary treatment (0/1). + X : array_like, shape (n, d_x) + Covariates. + """ + return _fit_oof_direct( + self, lambda: _TLNuisance(self.model_mu), np.asarray(Y), T, X=X, + cache_training_oof=True, + ) + + +# --------------------------------------------------------------------------- +# CrossFitSLearner +# --------------------------------------------------------------------------- + +class _SLNuisance: + """Nuisance for CrossFitSLearner: single pooled model mu(X, T).""" + + def __init__(self, overall_model): + self._overall_model = overall_model + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int).reshape(-1, 1) + Y_arr = np.asarray(Y).ravel() + X_arr = np.asarray(X) + feat = np.hstack([T_arr, X_arr, T_arr * X_arr]) + self._m = clone(self._overall_model, safe=False) + self._m.fit(feat, Y_arr) + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + n = X_arr.shape[0] + feat0 = np.hstack([np.zeros((n, 1)), X_arr, np.zeros_like(X_arr)]) + feat1 = np.hstack([np.ones((n, 1)), X_arr, X_arr]) + mu0 = self._m.predict(feat0) + mu1 = self._m.predict(feat1) + return mu0, mu1 + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class SLearner(_DirectNuisanceCateMixin, _BaseCrossfitEstimator): + """S-learner with cross-fitting. + + Fits a single pooled model mu(X, T) with treatment-covariate interactions + and predicts CATE directly as mu(X,1) - mu(X,0). + + Parameters + ---------- + overall_model : sklearn regressor + Pooled outcome model (features = [T, X, T*X]). + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, overall_model='auto', cv=3, + categories='auto', random_state=None): + self.overall_model = _resolve_continuous_nuisance_model(overall_model) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def _effect_from_nuisances(self, nuisances): + mu0, mu1 = nuisances + return mu1 - mu0 + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitSLearner.""" + return _fit_oof_direct( + self, lambda: _SLNuisance(self.overall_model), np.asarray(Y), T, X=X, + cache_training_oof=True, + ) + + +# --------------------------------------------------------------------------- +# CrossFitXLearner +# --------------------------------------------------------------------------- + +class _XLNuisance: + """Nuisance for CrossFitXLearner. + + Computes per-arm imputed effects (D_0, D_1) and propensity scores OOS. + """ + + def __init__(self, model_mu, propensity_model): + self._model_mu = model_mu + self._propensity_model = propensity_model + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int) + Y_arr = np.asarray(Y).ravel() + X_arr = np.asarray(X) + self._mu0, self._mu1, self._m0, self._m1 = _fit_mu_fold( + self._model_mu, Y_arr, T_arr, X_arr) + self._e, self._pm = _fit_propensity_fold(self._propensity_model, T_arr, X_arr) + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int) + Y_arr = np.asarray(Y).ravel() + X_arr = np.asarray(X) + n = X_arr.shape[0] + + mu0 = self._m0.predict(X_arr) + mu1 = self._m1.predict(X_arr) + e = np.clip( + self._pm.predict_proba(X_arr)[:, 1] + if hasattr(self._pm, 'predict_proba') + else self._pm.predict(X_arr), + _PROPENSITY_CLIP, 1 - _PROPENSITY_CLIP) + + # imputed effects per arm + D0 = np.where(T_arr == 0, mu1 - Y_arr, np.nan) # for control units + D1 = np.where(T_arr == 1, Y_arr - mu0, np.nan) # for treated units + return D0, D1, e + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class _XLFinal: + """Final stage for CrossFitXLearner. + + Fits per-arm CATE models on imputed effects, then combines via propensity. + """ + + def __init__(self, model_final, propensity_model): + self._model_final = model_final + self._propensity_model = propensity_model + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + D0, D1, e = nuisances + T_arr = np.asarray(T).ravel().astype(int) + X_arr = np.asarray(X) + + mask0 = ~np.isnan(D0) + mask1 = ~np.isnan(D1) + + self._m0 = clone(self._model_final, safe=False) + self._m1 = clone(self._model_final, safe=False) + + if mask0.sum() > 0: + self._m0.fit(X_arr[mask0], D0[mask0]) + if mask1.sum() > 0: + self._m1.fit(X_arr[mask1], D1[mask1]) + + self._pm = clone(self._propensity_model, safe=False) + self._pm.fit(X_arr, T_arr) + return self + + def predict(self, X=None): + X_arr = np.asarray(X) + tau0 = self._m0.predict(X_arr) + tau1 = self._m1.predict(X_arr) + if hasattr(self._pm, 'predict_proba'): + e = self._pm.predict_proba(X_arr)[:, 1] + else: + e = self._pm.predict(X_arr) + e = np.clip(np.asarray(e, dtype=float).ravel(), _PROPENSITY_CLIP, 1 - _PROPENSITY_CLIP) + return ((1 - e) * tau1 + e * tau0).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + return None + + +class XLearner(_BaseCrossfitEstimator): + """X-learner with cross-fitting. + + Computes per-arm imputed effects out-of-sample, fits per-arm CATE models, + and combines them via propensity weighting. + + Parameters + ---------- + model_mu : sklearn regressor + Outcome model fitted separately per arm. + propensity_model : sklearn classifier + Propensity model P(T=1|X). + model_final : sklearn regressor + Per-arm CATE model fitted on imputed effects. + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, model_mu='auto', propensity_model='auto', + model_final='auto', cv=3, categories='auto', random_state=None): + self.model_mu = _resolve_continuous_nuisance_model(model_mu) + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_final = _resolve_continuous_nuisance_model(model_final) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitXLearner.""" + return _fit_oof_two_stage( + self, + lambda: _XLNuisance(self.model_mu, self.propensity_model), + lambda: _XLFinal(self.model_final, self.propensity_model), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitIPTWLearner +# --------------------------------------------------------------------------- + +class _IPTWNuisance: + """Nuisance for CrossFitIPTWLearner: propensity e(X).""" + + def __init__(self, propensity_model): + self._propensity_model = propensity_model + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int) + X_arr = np.asarray(X) + self._e, self._pm = _fit_propensity_fold(self._propensity_model, T_arr, X_arr) + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + if hasattr(self._pm, 'predict_proba'): + e = self._pm.predict_proba(X_arr)[:, 1] + else: + e = self._pm.predict(X_arr) + return (np.clip(e, _PROPENSITY_CLIP, 1 - _PROPENSITY_CLIP),) + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class _IPTWFinal: + """Final stage for CrossFitIPTWLearner: regress phi = Y*(T/e - (1-T)/(1-e)) on X.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + (e,) = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + phi = Y_arr * (T_arr / e - (1 - T_arr) / (1 - e)) + self._model_cate.fit(X, phi) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + (e,) = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + phi = Y_arr * (T_arr / e - (1 - T_arr) / (1 - e)) + pred = self._model_cate.predict(X) + return np.mean((phi - pred) ** 2) + + +class IPTWLearner(_BaseCrossfitEstimator): + """IPTW learner with cross-fitting of propensity. + + Cross-fits the propensity model P(T=1|X), then regresses the IPTW + pseudo-outcome on X using the final CATE model. + + Parameters + ---------- + propensity_model : sklearn classifier, default LogisticRegression() + Model for P(T=1|X). + model_cate : sklearn regressor + Final CATE regression model. + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, propensity_model='auto', model_cate='auto', + cv=3, categories='auto', random_state=None): + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitIPTWLearner. + + Parameters + ---------- + Y : array_like, shape (n,) + Pre-computed CUT pseudo-outcome (e.g. from ``aipcw_cut_rmst``). + T : array_like, shape (n,) + Binary treatment (0/1). + X : array_like, shape (n, d_x) + Covariates. + """ + return _fit_oof_two_stage( + self, + lambda: _IPTWNuisance(self.propensity_model), + lambda: _IPTWFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitAIPTWLearner +# --------------------------------------------------------------------------- + +class _AIPTWFinal: + """Final stage for CrossFitAIPTWLearner: regress AIPTW pseudo-outcome on X.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + e, mu0, mu1 = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + phi = ( + Y_arr * (T_arr / e - (1 - T_arr) / (1 - e)) + + (1 - T_arr / e) * mu1 + - (1 - (1 - T_arr) / (1 - e)) * mu0 + ) + self._model_cate.fit(X, phi) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + return None + + +class AIPTWLearner(_BaseCrossfitEstimator): + """Augmented IPTW learner with cross-fitting. + + Cross-fits propensity and per-arm outcome models, builds the AIPTW + pseudo-outcome, and regresses that pseudo-outcome on X. + + Parameters + ---------- + propensity_model : sklearn classifier, default LogisticRegression() + model_mu : sklearn regressor + Per-arm outcome model. + model_cate : sklearn regressor + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, propensity_model='auto', model_mu='auto', + model_cate='auto', cv=3, categories='auto', random_state=None): + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_mu = _resolve_continuous_nuisance_model(model_mu) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitAIPTWLearner.""" + return _fit_oof_two_stage( + self, + lambda: _MCEANuisance(self.propensity_model, self.model_mu), + lambda: _AIPTWFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitMCLearner +# --------------------------------------------------------------------------- + +class _MCFinal: + """Final stage for CrossFitMCLearner: weighted regression of MC pseudo-outcome on X.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + (e,) = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + sign = 2 * T_arr - 1 + phi = 2 * sign * Y_arr + weights = sign * (T_arr - e) / (4 * e * (1 - e)) + self._model_cate.fit(X, phi, sample_weight=weights) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + return None + + +class MCLearner(_BaseCrossfitEstimator): + """Modified-covariate (MC) learner with cross-fitting. + + Cross-fits the propensity model, then performs weighted regression using + the MC pseudo-outcome and IPW weights. + + Parameters + ---------- + propensity_model : sklearn classifier, default LogisticRegression() + model_cate : sklearn regressor supporting ``sample_weight`` + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, propensity_model='auto', model_cate='auto', + cv=3, categories='auto', random_state=None): + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitMCLearner.""" + return _fit_oof_two_stage( + self, + lambda: _IPTWNuisance(self.propensity_model), + lambda: _MCFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitMCEALearner +# --------------------------------------------------------------------------- + +class _MCEANuisance: + """Nuisance for CrossFitMCEALearner: propensity e(X) + per-arm mu_0, mu_1.""" + + def __init__(self, propensity_model, model_mu): + self._propensity_model = propensity_model + self._model_mu = model_mu + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int) + Y_arr = np.asarray(Y).ravel() + X_arr = np.asarray(X) + self._e, self._pm = _fit_propensity_fold(self._propensity_model, T_arr, X_arr) + self._mu0, self._mu1, self._m0, self._m1 = _fit_mu_fold( + self._model_mu, Y_arr, T_arr, X_arr) + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + if hasattr(self._pm, 'predict_proba'): + e = self._pm.predict_proba(X_arr)[:, 1] + else: + e = self._pm.predict(X_arr) + e = np.clip(e, _PROPENSITY_CLIP, 1 - _PROPENSITY_CLIP) + mu0 = self._m0.predict(X_arr) + mu1 = self._m1.predict(X_arr) + return e, mu0, mu1 + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class _MCEAFinal: + """Final stage for CrossFitMCEALearner: augmented MC weighted regression.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + e, mu0, mu1 = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + residual = Y_arr - e * mu1 - (1 - e) * mu0 + sign = 2 * T_arr - 1 + phi = 2 * sign * residual + weights = sign * (T_arr - e) / (4 * e * (1 - e)) + self._model_cate.fit(X, phi, sample_weight=weights) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + return None + + +class MCEALearner(_BaseCrossfitEstimator): + """MC learner with efficiency augmentation and cross-fitting. + + Cross-fits propensity and per-arm outcome models, then performs augmented + MC weighted regression. + + Parameters + ---------- + propensity_model : sklearn classifier, default LogisticRegression() + model_mu : sklearn regressor + Per-arm outcome model. + model_cate : sklearn regressor supporting ``sample_weight`` + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, propensity_model='auto', model_mu='auto', + model_cate='auto', cv=3, categories='auto', random_state=None): + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_mu = _resolve_continuous_nuisance_model(model_mu) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitMCEALearner.""" + return _fit_oof_two_stage( + self, + lambda: _MCEANuisance(self.propensity_model, self.model_mu), + lambda: _MCEAFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitRALearner +# --------------------------------------------------------------------------- + +class _RAFinal: + """Final stage for CrossFitRALearner: regress RA pseudo-outcome on X.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + mu0, mu1 = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + phi = T_arr * (Y_arr - mu0) + (1 - T_arr) * (mu1 - Y_arr) + self._model_cate.fit(X, phi) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + return None + + +class RALearner(_BaseCrossfitEstimator): + """RA (regression-adjustment) learner with cross-fitting. + + Cross-fits per-arm outcome models, then regresses the RA pseudo-outcome + T*(Y - mu_0) + (1-T)*(mu_1 - Y) on X. + + Parameters + ---------- + model_mu : sklearn regressor + Per-arm outcome model. + model_cate : sklearn regressor + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, model_mu='auto', model_cate='auto', cv=3, + categories='auto', random_state=None): + self.model_mu = _resolve_continuous_nuisance_model(model_mu) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitRALearner.""" + return _fit_oof_two_stage( + self, + lambda: _TLNuisance(self.model_mu), + lambda: _RAFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitULearner +# --------------------------------------------------------------------------- + +class _UFinal: + """Final stage for CrossFitULearner: regress U pseudo-outcome on X.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + e, mu0, mu1 = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + denom = T_arr - e + # Avoid division by near-zero + safe = np.abs(denom) > 1e-6 + phi = np.where(safe, + (Y_arr - e * mu1 - (1 - e) * mu0) / np.where(safe, denom, 1.0), + 0.0) + self._model_cate.fit(X[safe], phi[safe]) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + return None + + +class ULearner(_BaseCrossfitEstimator): + """U-learner with cross-fitting. + + Cross-fits propensity and per-arm outcome models, then regresses the + U pseudo-outcome (Y - e*mu_1 - (1-e)*mu_0) / (T - e) on X. + + Parameters + ---------- + propensity_model : sklearn classifier, default LogisticRegression() + model_mu : sklearn regressor + model_cate : sklearn regressor + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, propensity_model='auto', model_mu='auto', + model_cate='auto', cv=3, categories='auto', random_state=None): + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_mu = _resolve_continuous_nuisance_model(model_mu) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitULearner.""" + return _fit_oof_two_stage( + self, + lambda: _MCEANuisance(self.propensity_model, self.model_mu), + lambda: _UFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitRLearner +# --------------------------------------------------------------------------- + +class _RFinal: + """Final stage for CrossFitRLearner: weighted regression of U pseudo-outcome on X.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + e, mu0, mu1 = nuisances + Y_arr = np.asarray(Y).ravel() + T_arr = np.asarray(T).ravel().astype(int) + denom = T_arr - e + safe = np.abs(denom) > 1e-6 + phi = np.zeros_like(Y_arr, dtype=float) + phi[safe] = (Y_arr[safe] - e[safe] * mu1[safe] - (1 - e[safe]) * mu0[safe]) / denom[safe] + weights = denom[safe] ** 2 + self._model_cate.fit(X[safe], phi[safe], sample_weight=weights) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + return None + + +class RLearner(_BaseCrossfitEstimator): + """R-learner with cross-fitting. + + Cross-fits propensity and per-arm outcome models, constructs the U-style + pseudo-outcome, and fits a weighted regression on X with weights + ``(T - e(X))^2``. + + Parameters + ---------- + propensity_model : sklearn classifier, default LogisticRegression() + model_mu : sklearn regressor + model_cate : sklearn regressor supporting ``sample_weight`` + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, propensity_model='auto', model_mu='auto', + model_cate='auto', cv=3, categories='auto', random_state=None): + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_mu = _resolve_continuous_nuisance_model(model_mu) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitRLearner.""" + return _fit_oof_two_stage( + self, + lambda: _MCEANuisance(self.propensity_model, self.model_mu), + lambda: _RFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, caching_stage_count=1, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + + +# --------------------------------------------------------------------------- +# CrossFitIFLearner +# --------------------------------------------------------------------------- + +class _IFFinal: + """Final stage for CrossFitIFLearner: regress UIF scores on X.""" + + def __init__(self, model_cate): + self._model_cate = clone(model_cate, safe=False) + + def fit(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + freq_weight=None, sample_var=None, groups=None): + # Y here IS the UIF score (pre-computed); nuisances = (e,) but unused in final stage + Y_arr = np.asarray(Y).ravel() + self._model_cate.fit(X, Y_arr) + return self + + def predict(self, X=None): + return self._model_cate.predict(X).reshape(-1, 1) + + def score(self, Y, T, *, X, W=None, Z=None, nuisances, sample_weight=None, + groups=None): + Y_arr = np.asarray(Y).ravel() + pred = self._model_cate.predict(X) + return np.mean((Y_arr - pred) ** 2) + + +class _NoopNuisance: + """No-op nuisance for learners whose pseudo-outcome is already complete.""" + + def fit(self, Y, T, *, X=None, W=None, Z=None, + sample_weight=None, groups=None): + return self + + def predict(self, Y, T, *, X=None, W=None, Z=None, sample_weight=None, groups=None): + return () + + def score(self, Y, T, *, X=None, W=None, Z=None, sample_weight=None, groups=None): + return () + + +class IFLearner(_BaseCrossfitEstimator): + """IF (Uncentered Influence Function) learner with cross-fitting. + + The input Y is pre-computed UIF scores (output of ``uif_diff_rmst`` or + ``uif_diff_rmtlj``). Cross-fitting the CATE regression avoids overfitting + to the already doubly-robust pseudo-outcomes. + + Parameters + ---------- + propensity_model : sklearn classifier, default LogisticRegression() + Accepted for API compatibility but not used. The UIF score already + encodes the treatment contrast. + model_cate : sklearn regressor + Final CATE model regressing UIF scores on X. + cv : int, default 3 + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, propensity_model='auto', model_cate='auto', + cv=3, categories='auto', random_state=None): + self.propensity_model = _resolve_binary_nuisance_model(propensity_model) + self.model_cate = _resolve_continuous_nuisance_model(model_cate) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def fit(self, Y, T=None, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitIFLearner. + + Parameters + ---------- + Y : array_like, shape (n,) + Pre-computed UIF scores (``uif_diff_rmst`` or ``uif_diff_rmtlj``). + T : array_like, shape (n,), optional + Accepted for API compatibility but ignored. If omitted, a dummy + treatment vector is supplied internally. + X : array_like, shape (n, d_x) + Covariates. + """ + if T is None: + T = (np.arange(np.asarray(Y).shape[0]) % 2).astype(int) + return _fit_oof_two_stage( + self, + lambda: _NoopNuisance(), + lambda: _IFFinal(self.model_cate), + np.asarray(Y), T, X=X, cache_training_oof=True, + ) + + def const_marginal_effect(self, X=None): + if _same_train_features(X, getattr(self, '_training_X_oof_', None)): + return np.asarray(self._training_oof_effect_).reshape(-1, 1) + return np.asarray(self.model_final_.predict(X)).reshape(-1, 1) + +# --------------------------------------------------------------------------- +# CrossFitSurvivalTLearner +# --------------------------------------------------------------------------- + +class _SurvivalTLNuisance: + """Nuisance for CrossFitSurvivalTLearner. + + Fits per-arm survival models on fold train data; predicts RMST values + out-of-sample on fold test data. + """ + + def __init__(self, models, tau): + self._models = models + self._tau = tau + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int) + X_arr = np.asarray(X) + + if hasattr(self._models, '__iter__') and not hasattr(self._models, 'fit'): + models_list = list(self._models) + if len(models_list) != 2: + raise ValueError("models must be a single estimator or list of exactly 2") + self._m0 = clone(models_list[0]) + self._m1 = clone(models_list[1]) + else: + self._m0 = clone(self._models) + self._m1 = clone(self._models) + + mask0 = T_arr == 0 + mask1 = T_arr == 1 + self._m0.fit(X_arr[mask0], Y[mask0]) + self._m1.fit(X_arr[mask1], Y[mask1]) + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + surv_a0 = self._m0.predict_survival_function(X_arr) + surv_a1 = self._m1.predict_survival_function(X_arr) + rmst_a0 = _compute_rmst(surv_a0, self._tau, getattr(self._m0, 'unique_times_', None)) + rmst_a1 = _compute_rmst(surv_a1, self._tau, getattr(self._m1, 'unique_times_', None)) + return rmst_a0, rmst_a1 + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class SurvivalTLearner(_DirectNuisanceCateMixin, _BaseCrossfitEstimator): + """T-learner for survival outcomes with cross-fitting. + + Fits per-arm survival models on held-out folds and predicts CATE directly + as RMST_a1(X) - RMST_a0(X). + + Parameters + ---------- + models : scikit-survival estimator or list of two estimators + Survival model(s) with scikit-survival API (fit / predict_survival_function). + tau : float + RMST time horizon. + cv : int, default 3 + Number of cross-fitting folds. + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, models='auto', tau, cv=3, + categories='auto', random_state=None): + self.models = _resolve_survival_nuisance_model(models) + self.tau = tau + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def _effect_from_nuisances(self, nuisances): + rmst_a0, rmst_a1 = nuisances + return rmst_a1 - rmst_a0 + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitSurvivalTLearner. + + Parameters + ---------- + Y : structured ndarray, dtype [('event', bool), ('time', float)] + Survival outcomes. + T : array_like, shape (n,) + Binary treatment (0/1). + X : array_like, shape (n, d_x) + Covariates. + """ + return _fit_oof_direct(self, lambda: _SurvivalTLNuisance(self.models, self.tau), Y, T, X=X) + + +# --------------------------------------------------------------------------- +# CrossFitSurvivalSLearner +# --------------------------------------------------------------------------- + +class _SurvivalSLNuisance: + """Nuisance for CrossFitSurvivalSLearner. + + Fits a single pooled survival model with [a, X, a*X] features on fold train; + predicts RMST for a=0 and a=1 on fold test. + """ + + def __init__(self, overall_model, tau): + self._overall_model = overall_model + self._tau = tau + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(float) + X_arr = np.asarray(X) + feat = _build_interaction_features(T_arr, X_arr) + self._m = clone(self._overall_model) + self._m.fit(feat, Y) + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + m = X_arr.shape[0] + feat_a0 = _build_interaction_features(np.zeros(m), X_arr) + feat_a1 = _build_interaction_features(np.ones(m), X_arr) + surv_a0 = self._m.predict_survival_function(feat_a0) + surv_a1 = self._m.predict_survival_function(feat_a1) + rmst_a0 = _compute_rmst(surv_a0, self._tau, getattr(self._m, 'unique_times_', None)) + rmst_a1 = _compute_rmst(surv_a1, self._tau, getattr(self._m, 'unique_times_', None)) + return rmst_a0, rmst_a1 + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class SurvivalSLearner(_DirectNuisanceCateMixin, _BaseCrossfitEstimator): + """S-learner for survival outcomes with cross-fitting. + + Fits a single pooled survival model with [a, X, a*X] features on held-out + folds and predicts CATE directly as RMST_a1(X) - RMST_a0(X). + + Parameters + ---------- + overall_model : scikit-survival estimator + Pooled survival model with scikit-survival API. + tau : float + RMST time horizon. + cv : int, default 3 + Number of cross-fitting folds. + categories : 'auto' or list, default 'auto' + random_state : int or None, default None + """ + + def __init__(self, *, overall_model='auto', tau, cv=3, + categories='auto', random_state=None): + self.overall_model = _resolve_survival_nuisance_model(overall_model) + self.tau = tau + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def _effect_from_nuisances(self, nuisances): + rmst_a0, rmst_a1 = nuisances + return rmst_a1 - rmst_a0 + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + """Fit CrossFitSurvivalSLearner. + + Parameters + ---------- + Y : structured ndarray, dtype [('event', bool), ('time', float)] + Survival outcomes. + T : array_like, shape (n,) + Binary treatment (0/1). + X : array_like, shape (n, d_x) + Covariates. + """ + return _fit_oof_direct(self, lambda: _SurvivalSLNuisance(self.overall_model, self.tau), Y, T, X=X) + + +# --------------------------------------------------------------------------- +# CrossFitCompetingRisksTLearner +# --------------------------------------------------------------------------- + +class _CompetingTLNuisance: + """Nuisance for CrossFitCompetingRisksTLearner. + + Fits per-arm overall and cause-specific survival models on fold-train data + and predicts restricted mean time lost (RMTL) for a=0 and a=1 on fold-test data. + """ + + def __init__(self, models, models_cause, tau, cause, + compute_separable=False, models_competing=None): + self._models = models + self._models_cause = models_cause + self._tau = tau + self._cause = cause + self._compute_separable = compute_separable + self._models_competing = models_competing + + def _clone_pair(self, estimator): + if hasattr(estimator, '__iter__') and not hasattr(estimator, 'fit'): + est_list = list(estimator) + if len(est_list) != 2: + raise ValueError("estimator must be a single estimator or list of exactly 2") + return clone(est_list[0]), clone(est_list[1]) + return clone(estimator), clone(estimator) + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(int) + X_arr = np.asarray(X) + event = np.asarray(Y['event']) + time = np.asarray(Y['time']) + + if np.unique(T_arr).size < 2: + raise ValueError("Both treatment arms must be present in each fold for CrossFitCompetingRisksTLearner.") + + self._time_grid = np.sort(np.unique(time)) + + self._overall0, self._overall1 = self._clone_pair(self._models) + self._cause0, self._cause1 = self._clone_pair(self._models_cause) + + mask0 = T_arr == 0 + mask1 = T_arr == 1 + + self._overall0 = _fit_survival_or_constant( + self._overall0, X_arr[mask0], time[mask0], event[mask0] != 0) + self._overall1 = _fit_survival_or_constant( + self._overall1, X_arr[mask1], time[mask1], event[mask1] != 0) + + self._cause0 = _fit_survival_or_constant( + self._cause0, X_arr[mask0], time[mask0], event[mask0] == self._cause) + self._cause1 = _fit_survival_or_constant( + self._cause1, X_arr[mask1], time[mask1], event[mask1] == self._cause) + + self._competing0 = None + self._competing1 = None + if self._compute_separable: + if self._models_competing is None: + raise ValueError("models_competing must be provided when compute_separable=True.") + self._competing0, self._competing1 = self._clone_pair(self._models_competing) + self._competing0 = _fit_survival_or_constant( + self._competing0, X_arr[mask0], time[mask0], event[mask0] > self._cause) + self._competing1 = _fit_survival_or_constant( + self._competing1, X_arr[mask1], time[mask1], event[mask1] > self._cause) + + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + S_overall_a0 = _eval_survival_on_grid(self._overall0, X_arr, self._time_grid) + S_overall_a1 = _eval_survival_on_grid(self._overall1, X_arr, self._time_grid) + S_cause_a0 = _eval_survival_on_grid(self._cause0, X_arr, self._time_grid) + S_cause_a1 = _eval_survival_on_grid(self._cause1, X_arr, self._time_grid) + + Fj_a0 = _compute_cif_on_grid(S_overall_a0, S_cause_a0) + Fj_a1 = _compute_cif_on_grid(S_overall_a1, S_cause_a1) + + rmtl_a0 = _compute_rmtl_from_cif(Fj_a0, self._time_grid, self._tau) + rmtl_a1 = _compute_rmtl_from_cif(Fj_a1, self._time_grid, self._tau) + return rmtl_a0, rmtl_a1 + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class CompetingRisksTLearner(_DirectNuisanceCateMixin, _BaseCrossfitEstimator): + """T-learner for competing risks outcomes with cross-fitting.""" + + def __init__(self, *, models='auto', models_cause='auto', tau, cause=1, + cv=3, categories='auto', random_state=None, + compute_separable=False, models_competing=None): + self.models = _resolve_survival_nuisance_model(models) + self.models_cause = _resolve_survival_nuisance_model(models_cause) + self.tau = tau + self.cause = cause + self.compute_separable = compute_separable + self.models_competing = _resolve_survival_nuisance_model(models_competing) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def _effect_from_nuisances(self, nuisances): + rmtl_a0, rmtl_a1 = nuisances + return rmtl_a1 - rmtl_a0 + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + if self.compute_separable and self.models_competing is None: + raise ValueError("models_competing must be provided when compute_separable=True.") + result = _fit_oof_direct( + self, + lambda: _CompetingTLNuisance( + self.models, + self.models_cause, + self.tau, + self.cause, + self.compute_separable, + self.models_competing, + ), + Y, T, X=X, + ) + self._training_X_separable_oof_ = None if X is None else np.array(X, copy=True) + self._training_oof_separable_direct_ = None + self._training_oof_separable_indirect_ = None + if self.compute_separable: + T_arr = np.asarray(T).ravel().astype(int) + X_arr = np.asarray(X) + event = np.asarray(Y['event']) + time = np.asarray(Y['time']) + mask0 = T_arr == 0 + mask1 = T_arr == 1 + self._sep_time_grid = np.sort(np.unique(time)) + self._sep_overall0 = _fit_survival_or_constant(self.models, X_arr[mask0], time[mask0], event[mask0] != 0) + self._sep_overall1 = _fit_survival_or_constant(self.models, X_arr[mask1], time[mask1], event[mask1] != 0) + self._sep_cause0 = _fit_survival_or_constant(self.models_cause, X_arr[mask0], time[mask0], event[mask0] == self.cause) + self._sep_cause1 = _fit_survival_or_constant(self.models_cause, X_arr[mask1], time[mask1], event[mask1] == self.cause) + self._sep_competing0 = _fit_survival_or_constant( + self.models_competing, X_arr[mask0], time[mask0], event[mask0] > self.cause) + self._sep_competing1 = _fit_survival_or_constant( + self.models_competing, X_arr[mask1], time[mask1], event[mask1] > self.cause) + if (X is not None) and (not getattr(self, '_skip_training_separable_oof_', False)): + direct, indirect = _cache_training_oof_separable(self, Y, T, X) + self._training_oof_separable_direct_ = direct + self._training_oof_separable_indirect_ = indirect + return result + + def _compute_separable_effects(self, X): + if not self.compute_separable: + raise RuntimeError("Separable effects available only when compute_separable=True.") + if not hasattr(self, '_sep_time_grid'): + raise RuntimeError("Call fit() before requesting separable effects.") + if _same_train_features(X, getattr(self, '_training_X_separable_oof_', None)): + return (np.asarray(self._training_oof_separable_direct_).ravel(), + np.asarray(self._training_oof_separable_indirect_).ravel()) + X_arr = np.asarray(X) + S_overall_a0 = _eval_survival_on_grid(self._sep_overall0, X_arr, self._sep_time_grid) + S_overall_a1 = _eval_survival_on_grid(self._sep_overall1, X_arr, self._sep_time_grid) + S_cause_a0 = _eval_survival_on_grid(self._sep_cause0, X_arr, self._sep_time_grid) + S_cause_a1 = _eval_survival_on_grid(self._sep_cause1, X_arr, self._sep_time_grid) + S_comp_a1 = _eval_survival_on_grid(self._sep_competing1, X_arr, self._sep_time_grid) + Fj_a0 = _compute_cif_on_grid(S_overall_a0, S_cause_a0) + Fj_a1 = _compute_cif_on_grid(S_overall_a1, S_cause_a1) + S_cross = np.clip(S_cause_a0 * S_comp_a1, 1e-3, 1.0) + Fj_cross = _compute_cif_on_grid(S_cross, S_cause_a0) + rmtl_a0 = _compute_rmtl_from_cif(Fj_a0, self._sep_time_grid, self.tau) + rmtl_a1 = _compute_rmtl_from_cif(Fj_a1, self._sep_time_grid, self.tau) + rmtl_cross = _compute_rmtl_from_cif(Fj_cross, self._sep_time_grid, self.tau) + return rmtl_a1 - rmtl_cross, rmtl_cross - rmtl_a0 + + +# --------------------------------------------------------------------------- +# CrossFitCompetingRisksSLearner +# --------------------------------------------------------------------------- + +class _CompetingSLNuisance: + """Nuisance for CrossFitCompetingRisksSLearner. + + Fits pooled overall and cause-specific survival models with [a, X, a*X] + features on fold-train data and predicts RMTL for a=0 and a=1 on fold-test data. + """ + + def __init__(self, overall_model, cause_model, tau, cause, + compute_separable=False, competing_model=None): + self._overall_model = overall_model + self._cause_model = cause_model + self._tau = tau + self._cause_id = cause + self._compute_separable = compute_separable + self._competing_model = competing_model + + def fit(self, Y, T, *, X, W=None, Z=None, + sample_weight=None, groups=None): + T_arr = np.asarray(T).ravel().astype(float) + X_arr = np.asarray(X) + event = np.asarray(Y['event']) + time = np.asarray(Y['time']) + + self._time_grid = np.sort(np.unique(time)) + + feat = _build_interaction_features(T_arr, X_arr) + + self._overall = _fit_survival_or_constant( + self._overall_model, feat, time, event != 0) + + self._cause = _fit_survival_or_constant( + self._cause_model, feat, time, event == self._cause_id) + + self._competing = None + if self._compute_separable: + if self._competing_model is None: + raise ValueError("competing_model must be provided when compute_separable=True.") + self._competing = _fit_survival_or_constant( + self._competing_model, feat, time, event > self._cause_id) + + return self + + def predict(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + X_arr = np.asarray(X) + m = X_arr.shape[0] + + feat_a0 = _build_interaction_features(np.zeros(m), X_arr) + feat_a1 = _build_interaction_features(np.ones(m), X_arr) + + S_overall_a0 = _eval_survival_on_grid(self._overall, feat_a0, self._time_grid) + S_overall_a1 = _eval_survival_on_grid(self._overall, feat_a1, self._time_grid) + + S_cause_a0 = _eval_survival_on_grid(self._cause, feat_a0, self._time_grid) + S_cause_a1 = _eval_survival_on_grid(self._cause, feat_a1, self._time_grid) + + Fj_a0 = _compute_cif_on_grid(S_overall_a0, S_cause_a0) + Fj_a1 = _compute_cif_on_grid(S_overall_a1, S_cause_a1) + + rmtl_a0 = _compute_rmtl_from_cif(Fj_a0, self._time_grid, self._tau) + rmtl_a1 = _compute_rmtl_from_cif(Fj_a1, self._time_grid, self._tau) + return rmtl_a0, rmtl_a1 + + def score(self, Y, T, *, X, W=None, Z=None, sample_weight=None, groups=None): + return None + + +class CompetingRisksSLearner(_DirectNuisanceCateMixin, _BaseCrossfitEstimator): + """S-learner for competing risks outcomes with cross-fitting.""" + + def __init__(self, *, overall_model='auto', cause_model='auto', tau, cause=1, + cv=3, categories='auto', random_state=None, + compute_separable=False, competing_model=None): + self.overall_model = _resolve_survival_nuisance_model(overall_model) + self.cause_model = _resolve_survival_nuisance_model(cause_model) + self.tau = tau + self.cause = cause + self.compute_separable = compute_separable + self.competing_model = _resolve_survival_nuisance_model(competing_model) + super().__init__(cv=cv, categories=categories, random_state=random_state) + + def _effect_from_nuisances(self, nuisances): + rmtl_a0, rmtl_a1 = nuisances + return rmtl_a1 - rmtl_a0 + + def fit(self, Y, T, *, X=None, W=None, groups=None, + cache_values=False, inference=None): + if self.compute_separable and self.competing_model is None: + raise ValueError("competing_model must be provided when compute_separable=True.") + result = _fit_oof_direct( + self, + lambda: _CompetingSLNuisance( + self.overall_model, + self.cause_model, + self.tau, + self.cause, + self.compute_separable, + self.competing_model, + ), + Y, T, X=X, + ) + self._training_X_separable_oof_ = None if X is None else np.array(X, copy=True) + self._training_oof_separable_direct_ = None + self._training_oof_separable_indirect_ = None + if self.compute_separable: + T_arr = np.asarray(T).ravel().astype(float) + X_arr = np.asarray(X) + event = np.asarray(Y['event']) + time = np.asarray(Y['time']) + feat = _build_interaction_features(T_arr, X_arr) + self._sep_time_grid = np.sort(np.unique(time)) + self._sep_overall = _fit_survival_or_constant(self.overall_model, feat, time, event != 0) + self._sep_cause = _fit_survival_or_constant(self.cause_model, feat, time, event == self.cause) + self._sep_competing = _fit_survival_or_constant(self.competing_model, feat, time, event > self.cause) + if (X is not None) and (not getattr(self, '_skip_training_separable_oof_', False)): + direct, indirect = _cache_training_oof_separable(self, Y, T, X) + self._training_oof_separable_direct_ = direct + self._training_oof_separable_indirect_ = indirect + return result + + def _compute_separable_effects(self, X): + if not self.compute_separable: + raise RuntimeError("Separable effects available only when compute_separable=True.") + if not hasattr(self, '_sep_time_grid'): + raise RuntimeError("Call fit() before requesting separable effects.") + if _same_train_features(X, getattr(self, '_training_X_separable_oof_', None)): + return (np.asarray(self._training_oof_separable_direct_).ravel(), + np.asarray(self._training_oof_separable_indirect_).ravel()) + X_arr = np.asarray(X) + m = X_arr.shape[0] + feat_a0 = _build_interaction_features(np.zeros(m), X_arr) + feat_a1 = _build_interaction_features(np.ones(m), X_arr) + S_overall_a0 = _eval_survival_on_grid(self._sep_overall, feat_a0, self._sep_time_grid) + S_overall_a1 = _eval_survival_on_grid(self._sep_overall, feat_a1, self._sep_time_grid) + S_cause_a0 = _eval_survival_on_grid(self._sep_cause, feat_a0, self._sep_time_grid) + S_cause_a1 = _eval_survival_on_grid(self._sep_cause, feat_a1, self._sep_time_grid) + S_comp_a1 = _eval_survival_on_grid(self._sep_competing, feat_a1, self._sep_time_grid) + Fj_a0 = _compute_cif_on_grid(S_overall_a0, S_cause_a0) + Fj_a1 = _compute_cif_on_grid(S_overall_a1, S_cause_a1) + S_cross = np.clip(S_cause_a0 * S_comp_a1, 1e-3, 1.0) + Fj_cross = _compute_cif_on_grid(S_cross, S_cause_a0) + rmtl_a0 = _compute_rmtl_from_cif(Fj_a0, self._sep_time_grid, self.tau) + rmtl_a1 = _compute_rmtl_from_cif(Fj_a1, self._sep_time_grid, self.tau) + rmtl_cross = _compute_rmtl_from_cif(Fj_cross, self._sep_time_grid, self.tau) + return rmtl_a1 - rmtl_cross, rmtl_cross - rmtl_a0 + + +class _SeparableAstar1CateMixin: + """Expose one separable astar1 estimand as the learner's main effect.""" + + _separable_component = None + + def _select_separable_component(self, direct, indirect): + if self._separable_component == 'direct': + return direct + if self._separable_component == 'indirect': + return indirect + raise RuntimeError("Unknown separable component.") + + def effect(self, X): + return np.asarray(self.const_marginal_effect(X)).ravel() + + def const_marginal_effect(self, X=None): + if X is None: + raise ValueError("X must be provided for separable-effect learners.") + X_arr = check_array(X, ensure_2d=True, dtype=float) + self._check_fitted_dims(X_arr) + if _same_train_features(X_arr, getattr(self, '_training_X_separable_oof_', None)): + direct = np.asarray(getattr(self, '_training_oof_separable_direct_', None)) + indirect = np.asarray(getattr(self, '_training_oof_separable_indirect_', None)) + else: + direct, indirect = self._compute_separable_effects(X_arr) + selected = self._select_separable_component(direct, indirect) + return np.asarray(selected, dtype=float).reshape(-1, 1) + + +class SeparableDirectAstar1TLearner(_SeparableAstar1CateMixin, CompetingRisksTLearner): + """Direct astar1 separable-effect T-learner for competing risks.""" + + _separable_component = 'direct' + + def __init__(self, *, models='auto', models_cause='auto', tau, cause=1, + cv=3, categories='auto', random_state=None, models_competing='auto'): + super().__init__( + models=models, + models_cause=models_cause, + tau=tau, + cause=cause, + cv=cv, + categories=categories, + random_state=random_state, + compute_separable=True, + models_competing=models_competing, + ) + + +class SeparableIndirectAstar1TLearner(_SeparableAstar1CateMixin, CompetingRisksTLearner): + """Indirect astar1 separable-effect T-learner for competing risks.""" + + _separable_component = 'indirect' + + def __init__(self, *, models='auto', models_cause='auto', tau, cause=1, + cv=3, categories='auto', random_state=None, models_competing='auto'): + super().__init__( + models=models, + models_cause=models_cause, + tau=tau, + cause=cause, + cv=cv, + categories=categories, + random_state=random_state, + compute_separable=True, + models_competing=models_competing, + ) + + +class SeparableDirectAstar1SLearner(_SeparableAstar1CateMixin, CompetingRisksSLearner): + """Direct astar1 separable-effect S-learner for competing risks.""" + + _separable_component = 'direct' + + def __init__(self, *, overall_model='auto', cause_model='auto', tau, cause=1, + cv=3, categories='auto', random_state=None, competing_model='auto'): + super().__init__( + overall_model=overall_model, + cause_model=cause_model, + tau=tau, + cause=cause, + cv=cv, + categories=categories, + random_state=random_state, + compute_separable=True, + competing_model=competing_model, + ) + + +class SeparableIndirectAstar1SLearner(_SeparableAstar1CateMixin, CompetingRisksSLearner): + """Indirect astar1 separable-effect S-learner for competing risks.""" + + _separable_component = 'indirect' + + def __init__(self, *, overall_model='auto', cause_model='auto', tau, cause=1, + cv=3, categories='auto', random_state=None, competing_model='auto'): + super().__init__( + overall_model=overall_model, + cause_model=cause_model, + tau=tau, + cause=cause, + cv=cv, + categories=categories, + random_state=random_state, + compute_separable=True, + competing_model=competing_model, + ) diff --git a/econml/tests/test_censor/__init__.py b/econml/tests/test_censor/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/econml/tests/test_censor/_helpers.py b/econml/tests/test_censor/_helpers.py new file mode 100644 index 000000000..7c67cf4c1 --- /dev/null +++ b/econml/tests/test_censor/_helpers.py @@ -0,0 +1,17 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +from sklearn.ensemble import GradientBoostingRegressor +from sklearn.linear_model import LogisticRegression, Ridge + + +def gbr(): + return GradientBoostingRegressor(n_estimators=20, max_depth=2, random_state=0) + + +def ridge(): + return Ridge(alpha=1.0) + + +def lr(): + return LogisticRegression(max_iter=200) diff --git a/econml/tests/test_censor/dgp.py b/econml/tests/test_censor/dgp.py new file mode 100644 index 000000000..b50079a68 --- /dev/null +++ b/econml/tests/test_censor/dgp.py @@ -0,0 +1,368 @@ +"""Survival DGP ported from original/simulation/survival/survival_hte_simulation_case1.R. + +Generates a single-failure survival dataset with: +- 6 covariates: x1,x2,x3 ~ MVN(0, Sigma), x4,x5,x6 ~ Bernoulli(0.5) +- Binary treatment with logistic propensity score +- Counterfactual T(a=0) ~ log-logistic; T(a=1) ~ log-normal +- Dependent censoring (lognormal for a=0, log-logistic for a=1) +- Administrative censoring at admin_cens +- True CATE = E[min(T(1),tau)] - E[min(T(0),tau)] (Monte-Carlo over 1000 reps) +""" + +import numpy as np + + +# --------------------------------------------------------------------------- +# Parametric survival time generators +# (mirror simsurv's loglogistic / lognormal AFT parameterisation) +# --------------------------------------------------------------------------- + +def _loglogistic_times(x0, covariates, betas, gamma, rng): + """Log-logistic AFT survival times. + + T = exp(eta) * W, W ~ Logistic(0,1) re-parameterised so that + the scale is exp(eta) and the shape is 1/gamma. + + Parameters + ---------- + x0 : float + Intercept value (constant column added to covariates). + covariates : ndarray, shape (n, p) + betas : dict key order matches [x0, x1, ..., xp] + gamma : float shape (> 0) + rng : np.random.Generator + + Returns + ------- + times : ndarray, shape (n,) + """ + n = covariates.shape[0] + beta_vals = np.array(list(betas.values())) # [x0, x1, ...] + X_aug = np.column_stack([np.full(n, x0), covariates]) + eta = X_aug @ beta_vals # linear predictor + + # Log-logistic: log T = eta + gamma * log(U/(1-U)), U ~ Uniform(0,1) + u = rng.uniform(0, 1, size=n) + log_t = eta + gamma * np.log(u / (1.0 - u)) + return np.exp(log_t) + + +def _lognormal_times(x0, covariates, betas, sigma, rng): + """Log-normal AFT survival times. + + T = exp(eta + sigma * Z), Z ~ N(0,1). + """ + n = covariates.shape[0] + beta_vals = np.array(list(betas.values())) + X_aug = np.column_stack([np.full(n, x0), covariates]) + eta = X_aug @ beta_vals + z = rng.standard_normal(size=n) + return np.exp(eta + sigma * z) + + +# --------------------------------------------------------------------------- +# True RMST by Monte-Carlo integration (mirror of R's 1000-rep approach) +# --------------------------------------------------------------------------- + +def _true_rmst_mc(covariates, betas_a0, gamma_a0, betas_a1, sigma_a1, tau, + n_mc=1000, seed=0): + """Compute E[min(T(a),tau)] for a=0 and a=1 by Monte-Carlo. + + Returns + ------- + rmst_a0 : ndarray (n,) + rmst_a1 : ndarray (n,) + """ + n = covariates.shape[0] + rng = np.random.default_rng(seed) + + sum_a0 = np.zeros(n) + sum_a1 = np.zeros(n) + for _ in range(n_mc): + ta0 = _loglogistic_times(1, covariates, betas_a0, gamma_a0, rng) + ta1 = _lognormal_times(1, covariates, betas_a1, sigma_a1, rng) + sum_a0 += np.minimum(ta0, tau) + sum_a1 += np.minimum(ta1, tau) + + return sum_a0 / n_mc, sum_a1 / n_mc + + +# --------------------------------------------------------------------------- +# Main DGP +# --------------------------------------------------------------------------- + +def make_survival_data(n=400, admin_cens=10.0, time_grid=0.01, tau=2.0, + seed=0, compute_true_cate=True): + """Generate a survival dataset matching case1 from the R simulation. + + Parameters + ---------- + n : int + Sample size. + admin_cens : float + Administrative censoring time. + time_grid : float + Rounding grid for observed times (mirrors R's ceiling(…/grid)*grid). + tau : float + RMST truncation horizon. + seed : int + Random seed. + compute_true_cate : bool + If True, attach ``true_cate`` (Monte-Carlo RMST differences, 1000 reps). + Set False for speed when not needed. + + Returns + ------- + data : dict with keys + X : ndarray (n, 6) covariates + T : ndarray (n,) binary treatment 0/1 + time : ndarray (n,) observed (possibly censored) time + event : ndarray (n,) event indicator 1=event, 0=censored + Y : structured ndarray dtype [('event',bool),('time',float)] + ps : ndarray (n,) true propensity score + true_cate : ndarray (n,) or None + """ + rng = np.random.default_rng(seed) + + # --- covariates --- + corr = np.array([[1.0, 0.5, 0.5], + [0.5, 1.0, 0.5], + [0.5, 0.5, 1.0]]) + L = np.linalg.cholesky(corr) + z = rng.standard_normal((n, 3)) + x_cont = z @ L.T # x1, x2, x3 + x_bin = rng.binomial(1, 0.5, size=(n, 3)) # x4, x5, x6 + X = np.column_stack([x_cont, x_bin]) + x1, x2, x3, x4, x5, x6 = X.T + + # --- propensity & treatment --- + logit_ps = 0.3 + 0.2*x1 + 0.3*x2 + 0.3*x3 - 0.2*x4 - 0.3*x5 - 0.2*x6 + ps = 1.0 / (1.0 + np.exp(-logit_ps)) + A = rng.binomial(1, ps).astype(float) + + # --- counterfactual event times --- + cov6 = X # (n, 6) + + betas_a0 = dict(x0=0.8, x1=-0.8, x2=1.0, x3=0.8, x4=0.4, x5=-0.4, x6=0.8) + betas_a1 = dict(x0=0.4, x1=0.6, x2=-0.8, x3=1.2, x4=0.6, x5=-0.3, x6=0.5) + + Ta0 = _loglogistic_times(1, cov6, betas_a0, gamma=0.2, rng=rng) + Ta1 = _lognormal_times(1, cov6, betas_a1, sigma=1.0, rng=rng) + + # round up to time_grid (mirror ceiling(…/grid)*grid in R) + Ta0 = np.ceil(Ta0 / time_grid) * time_grid + Ta1 = np.ceil(Ta1 / time_grid) * time_grid + Ta = (1 - A) * Ta0 + A * Ta1 + + # --- censoring (arm-dependent) --- + betas_c0 = dict(x0=1.8, x1=0.6, x2=-0.8, x3=0.5, x4=0.7, x5=-0.4, x6=-0.2) + betas_c1 = dict(x0=2.2, x1=0.6, x2=-0.8, x3=0.5, x4=0.7, x5=0.8, x6=1.2) + + C = np.empty(n) + mask0 = A == 0 + mask1 = A == 1 + + C[mask0] = _lognormal_times(1, cov6[mask0], betas_c0, sigma=0.8, rng=rng) + C[mask1] = _loglogistic_times(1, cov6[mask1], betas_c1, gamma=0.8, rng=rng) + + C = np.minimum(C, admin_cens) + + # --- observed time & event --- + time = np.ceil(np.minimum(C, Ta) / time_grid) * time_grid + event = (Ta <= C).astype(int) + + Y = np.array([(bool(e), float(t)) for e, t in zip(event, time)], + dtype=[('event', bool), ('time', float)]) + + # --- true CATE via Monte-Carlo --- + true_cate = None + if compute_true_cate: + rmst_a0, rmst_a1 = _true_rmst_mc(cov6, betas_a0, 0.2, betas_a1, 1.0, + tau, n_mc=1000, seed=seed + 1) + true_cate = rmst_a1 - rmst_a0 + + return dict(X=X, T=A, time=time, event=event, Y=Y, ps=ps, + true_cate=true_cate) + + +# --------------------------------------------------------------------------- +# Competing risks helpers +# --------------------------------------------------------------------------- + +def _exponential_times(x0, covariates, lambdas, betas, rng): + """Exponential survival times with covariate-dependent rate. + + T = -log(U) / (lambda * exp(X @ beta)), U ~ Uniform(0,1). + + Mirrors R's ``exponenetial()`` function (line 13 of competing_hte_simulation_helper.R). + """ + n = covariates.shape[0] + beta_vals = np.array(list(betas.values())) + X_aug = np.column_stack([np.full(n, x0), covariates]) + log_rate = X_aug @ beta_vals # log(lambda * exp(X@beta)) = log(lambda) + X@beta + u = rng.uniform(0, 1, size=n) + return -np.log(u) / (lambdas * np.exp(log_rate)) + + +def _true_rmtl_mc(covariates, tj1a0_params, tj2a0_params, tj1a1_params, tj2a1_params, + tau, n_mc=1000, seed=0): + """Monte-Carlo RMTL for competing risks (mirrors R lines 89-128 of case1.R). + + Computes E[(tau - min(T,tau)) * 1(J=1)] under four counterfactual scenarios: + (aj=0, ajbar=0), (aj=0, ajbar=1), (aj=1, ajbar=0), (aj=1, ajbar=1) + + Returns + ------- + rmtl_aj0_ajbar0, rmtl_aj0_ajbar1, rmtl_aj1_ajbar0, rmtl_aj1_ajbar1 : ndarray (n,) + """ + n = covariates.shape[0] + rng = np.random.default_rng(seed) + + sums = {k: np.zeros(n) for k in ('00', '01', '10', '11')} + for _ in range(n_mc): + tj1_a0 = _exponential_times(1, covariates, **tj1a0_params, rng=rng) + tj2_a0 = _exponential_times(1, covariates, **tj2a0_params, rng=rng) + tj1_a1 = _exponential_times(1, covariates, **tj1a1_params, rng=rng) + tj2_a1 = _exponential_times(1, covariates, **tj2a1_params, rng=rng) + + # aj=0 ajbar=0: both cause times from a=0 + T00 = np.minimum(tj1_a0, tj2_a0) + J00 = (tj1_a0 < tj2_a0).astype(int) + 1 * (tj1_a0 >= tj2_a0) * 2 + J00 = np.where(tj1_a0 < tj2_a0, 1, 2) + sums['00'] += (tau - np.minimum(T00, tau)) * (J00 == 1) + + # aj=0 ajbar=1: cause-j from a=0, competing from a=1 + T01 = np.minimum(tj1_a0, tj2_a1) + J01 = np.where(tj1_a0 < tj2_a1, 1, 2) + sums['01'] += (tau - np.minimum(T01, tau)) * (J01 == 1) + + # aj=1 ajbar=0: cause-j from a=1, competing from a=0 + T10 = np.minimum(tj1_a1, tj2_a0) + J10 = np.where(tj1_a1 < tj2_a0, 1, 2) + sums['10'] += (tau - np.minimum(T10, tau)) * (J10 == 1) + + # aj=1 ajbar=1: both from a=1 + T11 = np.minimum(tj1_a1, tj2_a1) + J11 = np.where(tj1_a1 < tj2_a1, 1, 2) + sums['11'] += (tau - np.minimum(T11, tau)) * (J11 == 1) + + return (sums['00'] / n_mc, sums['01'] / n_mc, + sums['10'] / n_mc, sums['11'] / n_mc) + + +# --------------------------------------------------------------------------- +# Competing risks DGP +# --------------------------------------------------------------------------- + +def make_competing_data(n=400, admin_cens=10.0, time_grid=0.01, tau=4.0, + seed=0, compute_true_cate=True): + """Generate a competing risks dataset matching case1 from the R simulation. + + Ports ``dgp_competing()`` from + ``original/simulation/competing/competing_hte_simulation_case1.R`` (lines 43–134). + + Parameters + ---------- + n : int + Sample size. + admin_cens : float + Administrative censoring time (default 10, as in R). + time_grid : float + Rounding grid for observed times (ceiling(…/grid)*grid). + tau : float + RMTL truncation horizon (R uses taus[4]=4). + seed : int + Random seed. + compute_true_cate : bool + If True, attach true CATE columns via 1000-rep Monte Carlo. + + Returns + ------- + data : dict with keys + X : ndarray (n, 6) covariates + T : ndarray (n,) binary treatment 0/1 + time : ndarray (n,) observed time + event : ndarray (n,) event indicator 0=censored, 1=cause1, 2=cause2 + Y : structured ndarray dtype [('event', int), ('time', float)] + ps : ndarray (n,) true propensity score + true_cate_total : ndarray (n,) or None RMTL(a=1) - RMTL(a=0) total + true_cate_direct : ndarray (n,) or None separable direct effect + true_cate_indirect: ndarray (n,) or None separable indirect effect + """ + rng = np.random.default_rng(seed) + + # --- covariates (identical to survival DGP) --- + corr = np.array([[1.0, 0.5, 0.5], + [0.5, 1.0, 0.5], + [0.5, 0.5, 1.0]]) + L = np.linalg.cholesky(corr) + z = rng.standard_normal((n, 3)) + x_cont = z @ L.T + x_bin = rng.binomial(1, 0.5, size=(n, 3)) + X = np.column_stack([x_cont, x_bin]) + x1, x2, x3, x4, x5, x6 = X.T + + # --- propensity & treatment (identical formula) --- + logit_ps = 0.3 + 0.2*x1 + 0.3*x2 + 0.3*x3 - 0.2*x4 - 0.3*x5 - 0.2*x6 + ps = 1.0 / (1.0 + np.exp(-logit_ps)) + A = rng.binomial(1, ps).astype(float) + + cov6 = X + + # --- cause 1 event times (exponential AFT) --- + tj1a0_params = dict(lambdas=0.12, betas=dict(x0=-0.1, x1=0.1, x2=-0.2, x3=0.2, x4=0.1, x5=0.8, x6=-0.2)) + tj1a1_params = dict(lambdas=0.15, betas=dict(x0=-0.3, x1=0.2, x2=-0.1, x3=0.4, x4=0.2, x5=0.3, x6=0.4)) + + # --- cause 2 event times (exponential AFT) --- + tj2a0_params = dict(lambdas=0.10, betas=dict(x0=0.0, x1=0.1, x2=-0.3, x3=0.1, x4=0.2, x5=0.4, x6=0.3)) + tj2a1_params = dict(lambdas=0.08, betas=dict(x0=-0.2, x1=0.2, x2=0.1, x3=-0.2, x4=0.4, x5=0.3, x6=0.6)) + + Tj1_a0 = _exponential_times(1, cov6, **tj1a0_params, rng=rng) + Tj1_a1 = _exponential_times(1, cov6, **tj1a1_params, rng=rng) + Tj2_a0 = _exponential_times(1, cov6, **tj2a0_params, rng=rng) + Tj2_a1 = _exponential_times(1, cov6, **tj2a1_params, rng=rng) + + # observed cause times (use actual arm) + Tj1 = (1 - A) * Tj1_a0 + A * Tj1_a1 + Tj2 = (1 - A) * Tj2_a0 + A * Tj2_a1 + + # round up to time_grid + Tj1 = np.ceil(Tj1 / time_grid) * time_grid + Tj2 = np.ceil(Tj2 / time_grid) * time_grid + + # --- censoring (arm-dependent) --- + betas_c0 = dict(x0=2.5, x1=0.6, x2=-0.4, x3=0.7, x4=1.5, x5=1.2, x6=1.6) + betas_c1 = dict(x0=2.0, x1=0.6, x2=0.8, x3=0.5, x4=1.2, x5=1.6, x6=1.2) + C = np.empty(n) + mask0 = A == 0 + mask1 = A == 1 + C[mask0] = _lognormal_times(1, cov6[mask0], betas_c0, sigma=0.8, rng=rng) + C[mask1] = _loglogistic_times(1, cov6[mask1], betas_c1, gamma=0.8, rng=rng) + C = np.minimum(C, admin_cens) + + # --- observed outcome --- + T_first = np.minimum(Tj1, Tj2) + time = np.ceil(np.minimum(C, T_first) / time_grid) * time_grid + # event: 0=censored, 1=cause1, 2=cause2 + event = np.where(T_first > C, 0, np.where(Tj1 < Tj2, 1, 2)).astype(int) + + Y = np.array([(int(e), float(t)) for e, t in zip(event, time)], + dtype=[('event', int), ('time', float)]) + + # --- true CATE via Monte-Carlo --- + true_cate_total = true_cate_direct = true_cate_indirect = None + if compute_true_cate: + rmtl_00, rmtl_01, rmtl_10, rmtl_11 = _true_rmtl_mc( + cov6, tj1a0_params, tj2a0_params, tj1a1_params, tj2a1_params, + tau, n_mc=1000, seed=seed + 1) + # total: (aj=1,ajbar=1) vs (aj=0,ajbar=0) + true_cate_total = rmtl_11 - rmtl_00 + # separable direct: (aj=1,ajbar=1) vs (aj=0,ajbar=1) + true_cate_direct = rmtl_11 - rmtl_01 + # separable indirect: (aj=0,ajbar=1) vs (aj=0,ajbar=0) + true_cate_indirect = rmtl_01 - rmtl_00 + + return dict(X=X, T=A, time=time, event=event, Y=Y, ps=ps, + true_cate_total=true_cate_total, + true_cate_direct=true_cate_direct, + true_cate_indirect=true_cate_indirect) diff --git a/econml/tests/test_censor/test_causal_survival_forest.py b/econml/tests/test_censor/test_causal_survival_forest.py new file mode 100644 index 000000000..cae3c317d --- /dev/null +++ b/econml/tests/test_censor/test_causal_survival_forest.py @@ -0,0 +1,341 @@ +"""Smoke tests for CausalSurvivalForest. + +DGP mirrors original/simulation/survival/survival_hte_simulation_case1.R +(log-logistic / log-normal event times, treatment-dependent censoring). +""" + +import unittest +import warnings +import numpy as np + +from econml.grf import CausalSurvivalForest, SurvivalForest, survival_forest +from econml.grf._causal_survival_forest import ( + _CausalSurvivalForestTrainer, + _ClusteredTreatmentForest, +) +from .dgp import make_survival_data + + +class _FailingSurvivalModel: + def get_params(self, deep=True): + return {} + + def set_params(self, **params): + return self + + def fit(self, X, y): + raise ValueError("intentional fold failure") + + +class TestCausalSurvivalForest(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_survival_data(n=300, tau=2.0, seed=42, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.time = data['time'] + cls.event = data['event'] + + def _make_est(self, **kwargs): + return CausalSurvivalForest( + horizon=2.0, + n_estimators=52, # must be divisible by subforest_size=4 → 52 = 4*13 + random_state=0, + **kwargs, + ) + + def test_fit_no_error(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + + def test_predict_shape(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + pred = est.predict(self.X) + self.assertEqual(pred.shape[0], self.X.shape[0]) + + def test_predict_finite(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + pred = est.predict(self.X) + self.assertTrue(np.all(np.isfinite(pred))) + + def test_tau_alias_matches_horizon(self): + est_tau = CausalSurvivalForest( + tau=2.0, + n_estimators=52, + random_state=0, + ) + est_tau.fit(self.X, self.T, self.time, self.event) + + est_horizon = CausalSurvivalForest( + horizon=2.0, + n_estimators=52, + random_state=0, + ) + est_horizon.fit(self.X, self.T, self.time, self.event) + + np.testing.assert_allclose(est_tau.effect(self.X), est_horizon.effect(self.X)) + + def test_nuisance_cv_two(self): + est = CausalSurvivalForest( + horizon=2.0, + nuisance_cv=2, + n_estimators=52, + random_state=0, + ) + est.fit(self.X, self.T, self.time, self.event) + self.assertEqual(est.effect(self.X).shape[0], self.X.shape[0]) + + def test_effect_uses_oob_on_training_data(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + effect = est.effect(self.X) + oob = np.asarray(est.oob_predict(self.X)).ravel() + np.testing.assert_allclose(effect, oob) + + def test_nuisance_stored(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + self.assertTrue(hasattr(est, 'W_hat_')) + self.assertTrue(hasattr(est, 'Y_hat_')) + self.assertTrue(hasattr(est, 'S_hat_')) + self.assertTrue(hasattr(est, 'C_hat_')) + self.assertTrue(hasattr(est, 'S1_hat_')) + self.assertTrue(hasattr(est, 'S0_hat_')) + self.assertEqual(est.W_hat_.shape, (self.X.shape[0],)) + self.assertEqual(est.Y_hat_.shape, (self.X.shape[0],)) + self.assertEqual(est.S_hat_.shape[0], self.X.shape[0]) + self.assertEqual(est.C_hat_.shape, est.S_hat_.shape) + self.assertEqual(est.S1_hat_.shape, est.S_hat_.shape) + self.assertEqual(est.S0_hat_.shape, est.S_hat_.shape) + self.assertTrue(np.all(np.isfinite(est.S_hat_))) + self.assertTrue(np.all(np.isfinite(est.C_hat_))) + + def test_propensity_in_unit_interval(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + self.assertTrue(np.all(est.W_hat_ >= 0)) + self.assertTrue(np.all(est.W_hat_ <= 1)) + + def test_default_propensity_uses_oob_predictions(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + in_sample = np.asarray(est.model_t_nuisance_.predict(self.X)).ravel() + in_sample = np.clip(in_sample, 1e-3, 1 - 1e-3) + self.assertFalse(np.allclose(est.W_hat_, in_sample)) + + def test_survival_probability_target(self): + est = CausalSurvivalForest( + horizon=2.0, + target="survival.probability", + n_estimators=52, + random_state=0, + ) + est.fit(self.X, self.T, self.time, self.event) + pred = est.predict(self.X) + self.assertEqual(pred.shape[0], self.X.shape[0]) + self.assertTrue(np.all(np.isfinite(pred))) + + def test_survival_nuisance_failure_falls_back(self): + est = CausalSurvivalForest( + horizon=2.0, + model_event=_FailingSurvivalModel(), + model_cens=_FailingSurvivalModel(), + n_estimators=52, + random_state=0, + ) + est.fit(self.X, self.T, self.time, self.event) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + self.assertTrue(np.allclose(est.S_hat_, 1.0)) + self.assertTrue(np.allclose(est.C_hat_, 1.0)) + + def test_psi_stored(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + self.assertTrue(hasattr(est, 'psi_numerator_')) + self.assertTrue(hasattr(est, 'psi_denominator_')) + self.assertTrue(np.all(np.isfinite(est.psi_numerator_))) + self.assertTrue(np.all(est.psi_denominator_ >= 0)) + + def test_predict_and_var_shape(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + point, var = est.predict_and_var(self.X[:17]) + self.assertEqual(point.shape, (17, 1)) + self.assertEqual(var.shape, (17, 1, 1)) + self.assertTrue(np.all(np.isfinite(point))) + self.assertTrue(np.all(np.isfinite(var))) + + def test_dedicated_final_trainer_used(self): + est = self._make_est() + est.fit(self.X, self.T, self.time, self.event) + self.assertIsInstance(est.final_trainer_, _CausalSurvivalForestTrainer) + + def test_invalid_horizon_raises(self): + data = make_survival_data(n=100, tau=2.0, seed=0, compute_true_cate=False) + X, T, time, event = data['X'], data['T'], data['time'], data['event'] + # horizon before first event → should raise + with self.assertRaises(ValueError): + CausalSurvivalForest(horizon=-1.0).fit(X, T, time, event) + + def test_conflicting_tau_and_horizon_raise(self): + with self.assertRaises(ValueError): + CausalSurvivalForest(horizon=2.0, tau=3.0) + + def test_all_censored_raises(self): + X = self.X.copy() + T = self.T.copy() + time = self.time.copy() + event = np.zeros_like(self.event) + with self.assertRaises(ValueError): + self._make_est().fit(X, T, time, event) + + def test_binary_treatment_only(self): + T = self.T.astype(float) + 0.1 + with self.assertRaises(NotImplementedError): + self._make_est().fit(self.X, T, self.time, self.event) + + def test_failure_times_arg(self): + grid = np.linspace(self.time.min(), self.time.max(), 12) + est = self._make_est(failure_times=grid) + est.fit(self.X, self.T, self.time, self.event) + self.assertEqual(est.S_hat_.shape[1], len(grid)) + + def test_cluster_arg_supported(self): + clusters = np.arange(self.X.shape[0]) % 11 + est = self._make_est(clusters=clusters) + est.fit(self.X, self.T, self.time, self.event) + self.assertEqual(est.effect(self.X).shape[0], self.X.shape[0]) + + def test_equalize_cluster_weights_supported(self): + clusters = np.arange(self.X.shape[0]) % 13 + est = self._make_est(clusters=clusters, equalize_cluster_weights=True) + est.fit(self.X, self.T, self.time, self.event) + self.assertEqual(est.effect(self.X).shape[0], self.X.shape[0]) + + def test_clustered_default_treatment_nuisance_used(self): + clusters = np.arange(self.X.shape[0]) % 11 + est = self._make_est(clusters=clusters) + est.fit(self.X, self.T, self.time, self.event) + self.assertIsInstance(est.model_t_nuisance_, _ClusteredTreatmentForest) + + def test_equalized_clusters_do_not_reweight_final_trainer_samples(self): + base_clusters = np.repeat(np.arange(15), np.array([5, 9, 11, 7, 13, 6, 10, 8, 12, 14, 5, 9, 7, 11, 13])) + clusters = np.resize(base_clusters, self.X.shape[0]) + est = self._make_est(clusters=clusters, equalize_cluster_weights=True) + est.fit(self.X, self.T, self.time, self.event) + self.assertIsNone(est.effective_sample_weight_) + np.testing.assert_allclose(est.final_trainer_.sample_weight_, est.psi_denominator_) + + def test_honesty_fraction_supported(self): + est = self._make_est(honesty_fraction=0.3) + est.fit(self.X, self.T, self.time, self.event) + self.assertAlmostEqual(est.final_trainer_.honesty_fraction, 0.3) + self.assertEqual(est.effect(self.X).shape[0], self.X.shape[0]) + + def test_honesty_prune_leaves_false_supported(self): + est = self._make_est(honesty_prune_leaves=False) + est.fit(self.X, self.T, self.time, self.event) + self.assertFalse(est.final_trainer_.honesty_prune_leaves) + self.assertEqual(est.effect(self.X).shape[0], self.X.shape[0]) + + def test_grf_control_args_affect_fit(self): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + est = self._make_est( + imbalance_penalty=0.5, + stabilize_splits=False, + fast_logrank=True, + tune_parameters="all", + tune_num_draws=4, + tune_num_reps=2, + tune_num_trees=16, + ) + est.fit(self.X, self.T, self.time, self.event) + self.assertEqual(len(caught), 0) + self.assertIsNotNone(est.tuned_model_t_params_) + self.assertIn("sample.fraction", est.tuned_model_t_params_) + self.assertIsNotNone(est.tuning_output_) + self.assertEqual(est.tuning_output_["metric"], "oob_mse") + self.assertEqual(est.tuning_output_["num.reps"], 2) + self.assertEqual(est.tuning_output_["num.trees"], 16) + self.assertGreaterEqual(len(est.tuning_output_["results"]), 1) + self.assertEqual(est.final_trainer_.imbalance_penalty, 0.5) + self.assertFalse(est.final_trainer_.stabilize_splits) + + def test_heavy_censoring_sparse_events_remains_finite(self): + rng = np.random.RandomState(123) + time = np.minimum(self.time, 0.35 + 0.05 * rng.rand(self.time.shape[0])) + event = ((self.event == 1) & (self.time <= 0.2)).astype(int) + event[:5] = 1 + est = self._make_est(tune_parameters="all", tune_num_draws=3, tune_num_trees=12) + est.fit(self.X, self.T, time, event) + pred = est.effect(self.X[:25]) + point, var = est.predict_and_var(self.X[:25]) + self.assertTrue(np.all(np.isfinite(pred))) + self.assertTrue(np.all(np.isfinite(point))) + self.assertTrue(np.all(np.isfinite(var))) + + +class TestSurvivalForest(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_survival_data(n=200, tau=2.0, seed=7, compute_true_cate=False) + cls.X = data["X"] + cls.time = data["time"] + cls.event = data["event"] + + def test_fit_and_oob_predict(self): + est = SurvivalForest(num_trees=40, seed=0) + est.fit(self.X, self.time, self.event) + oob = est.oob_predict(self.X) + self.assertEqual(oob.shape[0], self.X.shape[0]) + self.assertTrue(np.all(np.isfinite(oob))) + + def test_predict_returns_result(self): + est = SurvivalForest(num_trees=20, seed=0) + est.fit(self.X, self.time, self.event) + pred = est.predict(self.X[:10]) + self.assertEqual(pred.predictions.shape[0], 10) + self.assertTrue(np.all(np.isfinite(pred.predictions))) + + def test_survival_forest_function(self): + est = survival_forest(self.X, self.time, self.event, num_trees=20, seed=0) + self.assertIsInstance(est, SurvivalForest) + self.assertEqual(est.oob_predict(self.X).shape[0], self.X.shape[0]) + + def test_kaplan_meier_prediction_type(self): + est = SurvivalForest(num_trees=20, seed=0, prediction_type="Kaplan-Meier") + est.fit(self.X, self.time, self.event) + pred = est.predict(self.X[:5]) + self.assertEqual(pred.predictions.shape[0], 5) + self.assertTrue(np.all(np.isfinite(pred.predictions))) + + def test_cluster_weighting_supported(self): + clusters = np.arange(self.X.shape[0]) % 9 + est = SurvivalForest(num_trees=20, seed=0, clusters=clusters, equalize_cluster_weights=True) + est.fit(self.X, self.time, self.event) + self.assertEqual(est.oob_predict(self.X).shape[0], self.X.shape[0]) + + def test_honesty_fraction_supported(self): + est = SurvivalForest(num_trees=20, seed=0, honesty=True, honesty_fraction=0.3) + est.fit(self.X, self.time, self.event) + self.assertEqual(est.predict(self.X[:5]).predictions.shape[0], 5) + + def test_honesty_prune_leaves_false_supported(self): + est = SurvivalForest(num_trees=20, seed=0, honesty=True, honesty_prune_leaves=False) + est.fit(self.X, self.time, self.event) + self.assertEqual(est.oob_predict(self.X).shape[0], self.X.shape[0]) + + def test_fast_logrank_supported(self): + est = SurvivalForest(num_trees=20, seed=0, fast_logrank=True) + est.fit(self.X, self.time, self.event) + self.assertEqual(est.predict(self.X[:5]).predictions.shape[0], 5) + + +if __name__ == '__main__': + unittest.main() diff --git a/econml/tests/test_censor/test_competing_meta.py b/econml/tests/test_censor/test_competing_meta.py new file mode 100644 index 000000000..00b798d8a --- /dev/null +++ b/econml/tests/test_censor/test_competing_meta.py @@ -0,0 +1,198 @@ +"""Smoke tests for CompetingRisksTLearner and CompetingRisksSLearner. + +DGP mirrors original/simulation/competing/competing_hte_simulation_case1.R +(exponential cause times, treatment-dependent censoring, 3-state event indicator). + +Tests verify: + - fit() runs without error + - effect() returns shape (n,) + - ate() returns a scalar + - effect values are finite + - const_marginal_effect() returns shape (n, 1) +""" + +import unittest +import numpy as np +from sksurv.linear_model import CoxPHSurvivalAnalysis + +from econml.metalearners._censor_metalearners import ( + CompetingRisksTLearner, CompetingRisksSLearner, + SeparableDirectAstar1TLearner, SeparableDirectAstar1SLearner, + SeparableIndirectAstar1TLearner, SeparableIndirectAstar1SLearner, +) +from .dgp import make_competing_data + + +class TestCompetingRisksTLearner(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_competing_data(n=300, tau=4.0, seed=42, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.Y = data['Y'] + + def _make_model(self): + return CoxPHSurvivalAnalysis() + + def _make_est(self): + return CompetingRisksTLearner( + models=self._make_model(), + models_cause=self._make_model(), + tau=4.0, + ) + + def test_fit_effect_shape(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + # effect() squeezes d_t=1 dimension → (n,) per EconML convention + self.assertEqual(cate.shape, (self.X.shape[0],)) + + def test_effect_finite(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertTrue(np.all(np.isfinite(cate))) + + def test_ate_scalar(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + ate = est.ate(self.X) + self.assertEqual(np.asarray(ate).ndim, 0) + self.assertTrue(np.isfinite(ate)) + + def test_const_marginal_effect_shape(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + cme = est.const_marginal_effect(self.X) + self.assertEqual(cme.shape, (self.X.shape[0], 1)) + + def test_separable_direct_effect(self): + est = SeparableDirectAstar1TLearner( + models=self._make_model(), + models_cause=self._make_model(), + tau=4.0, + models_competing=self._make_model(), + ) + est.fit(self.Y, self.T, X=self.X) + direct_astar1 = est.effect(self.X) + self.assertEqual(direct_astar1.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(direct_astar1))) + + def test_separable_indirect_effect(self): + est = SeparableIndirectAstar1TLearner( + models=self._make_model(), + models_cause=self._make_model(), + tau=4.0, + models_competing=self._make_model(), + ) + est.fit(self.Y, self.T, X=self.X) + indirect_astar1 = est.effect(self.X) + self.assertEqual(indirect_astar1.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(indirect_astar1))) + + def test_default_models_competing(self): + direct_est = SeparableDirectAstar1TLearner(tau=4.0) + indirect_est = SeparableIndirectAstar1TLearner(tau=4.0) + direct_est.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + self.assertEqual(direct_est.effect(self.X).shape, (self.X.shape[0],)) + self.assertEqual(indirect_est.effect(self.X).shape, (self.X.shape[0],)) + + def test_default_survival_models(self): + est = CompetingRisksTLearner(tau=4.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(cate))) + + +class TestCompetingRisksSLearner(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_competing_data(n=300, tau=4.0, seed=42, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.Y = data['Y'] + + def _make_model(self): + return CoxPHSurvivalAnalysis() + + def _make_est(self): + return CompetingRisksSLearner( + overall_model=self._make_model(), + cause_model=self._make_model(), + tau=4.0, + ) + + def test_fit_effect_shape(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.X.shape[0],)) + + def test_effect_finite(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertTrue(np.all(np.isfinite(cate))) + + def test_ate_scalar(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + ate = est.ate(self.X) + self.assertEqual(np.asarray(ate).ndim, 0) + self.assertTrue(np.isfinite(ate)) + + def test_const_marginal_effect_shape(self): + est = self._make_est() + est.fit(self.Y, self.T, X=self.X) + cme = est.const_marginal_effect(self.X) + self.assertEqual(cme.shape, (self.X.shape[0], 1)) + + def test_separable_direct_effect(self): + est = SeparableDirectAstar1SLearner( + overall_model=self._make_model(), + cause_model=self._make_model(), + tau=4.0, + competing_model=self._make_model(), + ) + est.fit(self.Y, self.T, X=self.X) + direct_astar1 = est.effect(self.X) + self.assertEqual(direct_astar1.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(direct_astar1))) + + def test_separable_indirect_effect(self): + est = SeparableIndirectAstar1SLearner( + overall_model=self._make_model(), + cause_model=self._make_model(), + tau=4.0, + competing_model=self._make_model(), + ) + est.fit(self.Y, self.T, X=self.X) + indirect_astar1 = est.effect(self.X) + self.assertEqual(indirect_astar1.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(indirect_astar1))) + + def test_default_competing_model(self): + direct_est = SeparableDirectAstar1SLearner(tau=4.0) + indirect_est = SeparableIndirectAstar1SLearner(tau=4.0) + direct_est.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + self.assertEqual(direct_est.effect(self.X).shape, (self.X.shape[0],)) + self.assertEqual(indirect_est.effect(self.X).shape, (self.X.shape[0],)) + + def test_default_survival_models(self): + est = CompetingRisksSLearner(tau=4.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(cate))) + + +if __name__ == '__main__': + unittest.main() diff --git a/econml/tests/test_censor/test_crossfit_meta.py b/econml/tests/test_censor/test_crossfit_meta.py new file mode 100644 index 000000000..abefc71b9 --- /dev/null +++ b/econml/tests/test_censor/test_crossfit_meta.py @@ -0,0 +1,756 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +"""Smoke tests for CrossFit* metalearners. + +Tests verify: + - fit() runs without error + - effect() returns shape (n,) + - effect values are finite + - cv=2 and cv=3 both work +""" + +import unittest +import numpy as np + +from econml.metalearners._censor_metalearners import ( + TLearner, + SLearner, + XLearner, + IPTWLearner, + AIPTWLearner, + MCLearner, + MCEALearner, + RLearner, + RALearner, + ULearner, + IFLearner, + SurvivalTLearner, + SurvivalSLearner, + CompetingRisksTLearner, + CompetingRisksSLearner, + SeparableDirectAstar1TLearner, + SeparableIndirectAstar1TLearner, + SeparableDirectAstar1SLearner, + SeparableIndirectAstar1SLearner, +) +from econml.censor import fit_nuisance_survival, aipcw_cut_rmst, uif_diff_rmst +from .dgp import make_survival_data, make_competing_data + + +# --------------------------------------------------------------------------- +# Shared fixture: generate survival data + AIPCW pseudo-outcome +# --------------------------------------------------------------------------- + +class _CrossFitBase(unittest.TestCase): + """Base class: survival data + pre-computed AIPCW + UIF pseudo-outcomes.""" + + @classmethod + def setUpClass(cls): + from sksurv.linear_model import CoxPHSurvivalAnalysis + data = make_survival_data(n=360, tau=2.0, seed=42, compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.time = data['time'] + cls.event = data['event'] + cls.Y = data['Y'] # structured array for survival learners + cls.n = cls.X.shape[0] + + cox = CoxPHSurvivalAnalysis() + nuis = fit_nuisance_survival( + cls.time, cls.event, cls.T, cls.X, + model_censoring=cox, + model_event=cox, + ) + cls.Y_star = aipcw_cut_rmst( + cls.T, cls.time, cls.event, 2.0, + nuis.G_a0, nuis.G_a1, nuis.S_a0, nuis.S_a1, + time_grid=nuis.time_grid, + ) + # UIF scores for IFLearner test + ps = data.get('ps', 0.5 * np.ones(cls.n)) + bw = 1.0 / (ps * cls.T + (1 - ps) * (1 - cls.T)) + tilt = np.ones(cls.n) + cls.Y_uif = uif_diff_rmst( + cls.T, cls.time, cls.event, 2.0, bw, tilt, + nuis.G_a0, nuis.G_a1, nuis.S_a0, nuis.S_a1, + time_grid=nuis.time_grid, + ) + + +# --------------------------------------------------------------------------- +# CrossFitTLearner +# --------------------------------------------------------------------------- + +class TestCrossFitTLearner(_CrossFitBase): + + def _est(self, cv=2): + return TLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + def test_cv_one_raises(self): + est = self._est(cv=1) + with self.assertRaisesRegex(ValueError, "cv >= 2"): + est.fit(self.Y_star, self.T, X=self.X) + + +# --------------------------------------------------------------------------- +# CrossFitSLearner +# --------------------------------------------------------------------------- + +class TestCrossFitSLearner(_CrossFitBase): + + def _est(self, cv=2): + return SLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitXLearner +# --------------------------------------------------------------------------- + +class TestCrossFitXLearner(_CrossFitBase): + + def _est(self, cv=2): + return XLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + +# --------------------------------------------------------------------------- +# CrossFitIPTWLearner +# --------------------------------------------------------------------------- + +class TestCrossFitIPTWLearner(_CrossFitBase): + + def _est(self, cv=2): + return IPTWLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitAIPTWLearner +# --------------------------------------------------------------------------- + +class TestCrossFitAIPTWLearner(_CrossFitBase): + + def _est(self, cv=2): + return AIPTWLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitMCLearner +# --------------------------------------------------------------------------- + +class TestCrossFitMCLearner(_CrossFitBase): + + def _est(self, cv=2): + return MCLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitMCEALearner +# --------------------------------------------------------------------------- + +class TestCrossFitMCEALearner(_CrossFitBase): + + def _est(self, cv=2): + return MCEALearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitRALearner +# --------------------------------------------------------------------------- + +class TestCrossFitRALearner(_CrossFitBase): + + def _est(self, cv=2): + return RALearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitULearner +# --------------------------------------------------------------------------- + +class TestCrossFitULearner(_CrossFitBase): + + def _est(self, cv=2): + return ULearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitRLearner +# --------------------------------------------------------------------------- + +class TestCrossFitRLearner(_CrossFitBase): + + def _est(self, cv=2): + return RLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_star, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitIFLearner +# --------------------------------------------------------------------------- + +class TestCrossFitIFLearner(_CrossFitBase): + + def _est(self, cv=2): + return IFLearner(cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y_uif, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y_uif, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_fit_without_treatment(self): + est = self._est() + est.fit(self.Y_uif, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + self.assertTrue(np.all(np.isfinite(cate))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y_uif, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y_uif, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitSurvivalTLearner +# --------------------------------------------------------------------------- + +class TestCrossFitSurvivalTLearner(_CrossFitBase): + + def _make_surv(self): + from sksurv.ensemble import RandomSurvivalForest + return RandomSurvivalForest(n_estimators=20, min_samples_leaf=5, random_state=0) + + def _est(self, cv=2): + return SurvivalTLearner(tau=2.0, cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitSurvivalSLearner +# --------------------------------------------------------------------------- + +class TestCrossFitSurvivalSLearner(_CrossFitBase): + + def _make_surv(self): + from sksurv.ensemble import RandomSurvivalForest + return RandomSurvivalForest(n_estimators=20, min_samples_leaf=5, random_state=0) + + def _est(self, cv=2): + return SurvivalSLearner(tau=2.0, cv=cv, random_state=0) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + +# --------------------------------------------------------------------------- +# CrossFitCompetingRisks Learners +# --------------------------------------------------------------------------- + +class _CrossFitCompetingBase(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_competing_data(n=360, tau=4.0, seed=123, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.Y = data['Y'] + cls.n = cls.X.shape[0] + cls.tau = 4.0 + + def _cox(self): + from sksurv.linear_model import CoxPHSurvivalAnalysis + return CoxPHSurvivalAnalysis() + + +class TestCrossFitCompetingRisksTLearner(_CrossFitCompetingBase): + + def _est(self, cv=2, separable=False): + kwargs = {} + if separable: + kwargs.update( + compute_separable=True, + models_competing=self._cox(), + ) + return CompetingRisksTLearner( + tau=self.tau, + cv=cv, + random_state=0, + **kwargs, + ) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_separable_effects(self): + direct_est = SeparableDirectAstar1TLearner( + tau=self.tau, cv=2, random_state=0 + ) + indirect_est = SeparableIndirectAstar1TLearner( + tau=self.tau, cv=2, random_state=0 + ) + direct_est.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + direct = direct_est.effect(self.X) + indirect = indirect_est.effect(self.X) + self.assertEqual(direct.shape, (self.n,)) + self.assertEqual(indirect.shape, (self.n,)) + self.assertTrue(np.all(np.isfinite(direct))) + self.assertTrue(np.all(np.isfinite(indirect))) + + def test_separable_effects_training_is_oof(self): + direct_est = SeparableDirectAstar1TLearner( + tau=self.tau, cv=2, random_state=0 + ) + indirect_est = SeparableIndirectAstar1TLearner( + tau=self.tau, cv=2, random_state=0 + ) + direct_est.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(direct_est.effect(self.X), direct_est._training_oof_separable_direct_) + np.testing.assert_allclose(indirect_est.effect(self.X), indirect_est._training_oof_separable_indirect_) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + def test_default_models_competing(self): + est = CompetingRisksTLearner( + tau=self.tau, + cv=2, + random_state=0, + compute_separable=True, + ) + est.fit(self.Y, self.T, X=self.X) + self.assertEqual(est._training_oof_separable_direct_.shape, (self.n,)) + self.assertEqual(est._training_oof_separable_indirect_.shape, (self.n,)) + + +class TestCrossFitCompetingRisksSLearner(_CrossFitCompetingBase): + + def _est(self, cv=2, separable=False): + kwargs = {} + if separable: + kwargs.update( + compute_separable=True, + competing_model=self._cox(), + ) + return CompetingRisksSLearner( + tau=self.tau, + cv=cv, + random_state=0, + **kwargs, + ) + + def test_fit_effect_shape(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_cv_parameter(self): + for cv in (2,): + est = self._est(cv=cv) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_separable_effects(self): + direct_est = SeparableDirectAstar1SLearner( + tau=self.tau, cv=2, random_state=0 + ) + indirect_est = SeparableIndirectAstar1SLearner( + tau=self.tau, cv=2, random_state=0 + ) + direct_est.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + direct = direct_est.effect(self.X) + indirect = indirect_est.effect(self.X) + self.assertEqual(direct.shape, (self.n,)) + self.assertEqual(indirect.shape, (self.n,)) + + +class TestSeparableAstar1TLearners(_CrossFitCompetingBase): + + def test_direct_effect_matches_separable_direct(self): + base = CompetingRisksTLearner( + tau=self.tau, cv=2, random_state=0, compute_separable=True + ) + direct_est = SeparableDirectAstar1TLearner( + tau=self.tau, cv=2, random_state=0 + ) + base.fit(self.Y, self.T, X=self.X) + direct_est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(direct_est.effect(self.X), base._training_oof_separable_direct_) + + def test_indirect_effect_matches_separable_indirect(self): + base = CompetingRisksTLearner( + tau=self.tau, cv=2, random_state=0, compute_separable=True + ) + indirect_est = SeparableIndirectAstar1TLearner( + tau=self.tau, cv=2, random_state=0 + ) + base.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(indirect_est.effect(self.X), base._training_oof_separable_indirect_) + + +class TestSeparableAstar1SLearners(_CrossFitCompetingBase): + + def _est(self, cv=2, separable=True): + return CompetingRisksSLearner( + tau=self.tau, cv=cv, random_state=0, compute_separable=separable + ) + + def test_direct_effect_matches_separable_direct(self): + base = CompetingRisksSLearner( + tau=self.tau, cv=2, random_state=0, compute_separable=True + ) + direct_est = SeparableDirectAstar1SLearner( + tau=self.tau, cv=2, random_state=0 + ) + base.fit(self.Y, self.T, X=self.X) + direct_est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(direct_est.effect(self.X), base._training_oof_separable_direct_) + + def test_indirect_effect_matches_separable_indirect(self): + base = CompetingRisksSLearner( + tau=self.tau, cv=2, random_state=0, compute_separable=True + ) + indirect_est = SeparableIndirectAstar1SLearner( + tau=self.tau, cv=2, random_state=0 + ) + base.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(indirect_est.effect(self.X), base._training_oof_separable_indirect_) + self.assertTrue(np.all(np.isfinite(base._training_oof_separable_direct_))) + self.assertTrue(np.all(np.isfinite(base._training_oof_separable_indirect_))) + + def test_separable_effects_training_is_oof(self): + direct_est = SeparableDirectAstar1SLearner( + tau=self.tau, cv=2, random_state=0 + ) + indirect_est = SeparableIndirectAstar1SLearner( + tau=self.tau, cv=2, random_state=0 + ) + direct_est.fit(self.Y, self.T, X=self.X) + indirect_est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(direct_est.effect(self.X), direct_est._training_oof_separable_direct_) + np.testing.assert_allclose(indirect_est.effect(self.X), indirect_est._training_oof_separable_indirect_) + + def test_training_effect_is_oof(self): + est = self._est() + est.fit(self.Y, self.T, X=self.X) + np.testing.assert_allclose(est.effect(self.X), est._training_oof_effect_) + + def test_default_competing_model(self): + est = CompetingRisksSLearner( + tau=self.tau, + cv=2, + random_state=0, + compute_separable=True, + ) + est.fit(self.Y, self.T, X=self.X) + self.assertEqual(est._training_oof_separable_direct_.shape, (self.n,)) + self.assertEqual(est._training_oof_separable_indirect_.shape, (self.n,)) diff --git a/econml/tests/test_censor/test_nuisance.py b/econml/tests/test_censor/test_nuisance.py new file mode 100644 index 000000000..6a6fb2390 --- /dev/null +++ b/econml/tests/test_censor/test_nuisance.py @@ -0,0 +1,403 @@ +"""Tests for nuisance survival model fitting utility. + +Tests verify: + - fit_nuisance_survival runs without error with CoxPH models + - Returns correct matrix shapes (n, ns) + - Survival matrices are in valid range [1e-3, 1] + - Only requested models are fitted (others are None) + - Works for both survival (single-failure) and competing risks data + - End-to-end: nuisance → CUT transform pipeline works + - Public nuisance helpers are OOS-only +""" + +import unittest +import numpy as np +from sklearn.linear_model import LogisticRegression +from sksurv.linear_model import CoxPHSurvivalAnalysis + +from econml.censor._nuisance import ( + fit_nuisance_survival, + fit_nuisance_survival_crossfit, + fit_nuisance_competing_crossfit, + CrossFitNuisanceResult, + _make_sksurv_y, +) +from .dgp import make_survival_data, make_competing_data + + +class TestMakeSksuvY(unittest.TestCase): + + def test_dtype(self): + y = _make_sksurv_y(np.array([1.0, 2.0]), np.array([True, False])) + self.assertEqual(y.dtype.names, ('event', 'time')) + self.assertTrue(y['event'][0]) + self.assertFalse(y['event'][1]) + + def test_values(self): + y = _make_sksurv_y(np.array([3.5, 7.2]), np.array([False, True])) + self.assertAlmostEqual(y['time'][0], 3.5) + self.assertAlmostEqual(y['time'][1], 7.2) + + +class TestFitNuisanceSurvival(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_survival_data(n=300, tau=4.0, seed=42, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.time = data['time'] + cls.event = data['event'].astype(int) + cls.n = len(cls.T) + + def test_censoring_only(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=None, + model_cause=None, + model_competing=None, + propensity_model=None) + + self.assertIsNotNone(result.G_a0) + self.assertIsNotNone(result.G_a1) + self.assertIsNone(result.S_a0) + self.assertIsNone(result.Sj_a0) + + ns = len(result.time_grid) + self.assertEqual(result.G_a0.shape, (self.n, ns)) + self.assertEqual(result.G_a1.shape, (self.n, ns)) + + def test_event_only(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_censoring=None, + model_event=CoxPHSurvivalAnalysis(), + model_cause=None, + model_competing=None, + propensity_model=None) + + self.assertIsNotNone(result.S_a0) + self.assertIsNotNone(result.S_a1) + self.assertIsNone(result.G_a0) + + def test_both_censoring_and_event(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis()) + + self.assertIsNotNone(result.G_a0) + self.assertIsNotNone(result.S_a0) + + def test_survival_values_in_range(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis()) + + for mat in [result.G_a0, result.G_a1, result.S_a0, result.S_a1]: + self.assertTrue(np.all(mat >= 1e-3)) + self.assertTrue(np.all(mat <= 1.0)) + self.assertTrue(np.all(np.isfinite(mat))) + + def test_custom_time_grid(self): + grid = np.linspace(0.5, 8.0, 50) + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_event=CoxPHSurvivalAnalysis(), + time_grid=grid) + + np.testing.assert_array_equal(result.time_grid, grid) + self.assertEqual(result.S_a0.shape[1], 50) + + def test_x_pred(self): + with self.assertRaisesRegex(ValueError, "OOS-only"): + fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_event=CoxPHSurvivalAnalysis(), + X_pred=self.X[:10]) + + def test_matches_crossfit_wrapper(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + cv=2, + random_state=123) + result_cf = fit_nuisance_survival_crossfit( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + cv=2, + random_state=123) + + np.testing.assert_allclose(result.G_a0, result_cf.G_a0) + np.testing.assert_allclose(result.G_a1, result_cf.G_a1) + np.testing.assert_allclose(result.S_a0, result_cf.S_a0) + np.testing.assert_allclose(result.S_a1, result_cf.S_a1) + np.testing.assert_array_equal(result.time_grid, result_cf.time_grid) + + def test_wrapper_propagates_propensity_outputs(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + propensity_model=LogisticRegression(max_iter=1000), + cv=2, + random_state=123) + + self.assertIsInstance(result, CrossFitNuisanceResult) + self.assertEqual(result.ps.shape, (self.n,)) + self.assertEqual(result.iptw.shape, (self.n,)) + self.assertEqual(result.naive.shape, (self.n,)) + self.assertTrue(np.all(np.isfinite(result.ps))) + self.assertTrue(np.all(np.isfinite(result.iptw))) + + def test_default_auto_models_fit(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_cause=None, + model_competing=None, + cv=2, + random_state=123) + + self.assertIsNotNone(result.G_a0) + self.assertIsNotNone(result.S_a0) + self.assertIsNotNone(result.ps) + self.assertIsNone(result.Sj_a0) + self.assertIsNone(result.Sjbar_a0) + + +class TestFitNuisanceCompetingRisks(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_competing_data(n=300, tau=4.0, seed=42, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.time = data['time'] + cls.event = data['event'] + cls.n = len(cls.T) + + def test_all_four_models(self): + cox = CoxPHSurvivalAnalysis() + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_censoring=cox, + model_event=cox, + model_cause=cox, + model_competing=cox, + cause=1) + + ns = len(result.time_grid) + for attr in ['G_a0', 'G_a1', 'S_a0', 'S_a1', + 'Sj_a0', 'Sj_a1', 'Sjbar_a0', 'Sjbar_a1']: + mat = getattr(result, attr) + self.assertIsNotNone(mat, f"{attr} should not be None") + self.assertEqual(mat.shape, (self.n, ns), f"{attr} shape mismatch") + self.assertTrue(np.all(np.isfinite(mat)), f"{attr} has non-finite values") + + def test_cause_only(self): + result = fit_nuisance_survival( + self.time, self.event, self.T, self.X, + model_cause=CoxPHSurvivalAnalysis(), + model_censoring=None, + model_event=None, + model_competing=None, + propensity_model=None, + cause=1) + + self.assertIsNotNone(result.Sj_a0) + self.assertIsNotNone(result.Sj_a1) + self.assertIsNone(result.G_a0) + self.assertIsNone(result.S_a0) + self.assertIsNone(result.Sjbar_a0) + + def test_competing_defaults_fit(self): + result = fit_nuisance_competing_crossfit( + self.time, self.event, self.T, self.X, + cv=2, + random_state=123) + + self.assertIsNotNone(result.G_a0) + self.assertIsNotNone(result.S_a0) + self.assertIsNotNone(result.Sj_a0) + self.assertIsNotNone(result.Sjbar_a0) + self.assertIsNotNone(result.ps) + + +class TestEndToEnd(unittest.TestCase): + """Test the full pipeline: nuisance → CUT transform.""" + + def test_nuisance_to_ipcw(self): + from econml.censor import ipcw_cut_rmst + + data = make_survival_data(n=200, tau=4.0, seed=99, + compute_true_cate=False) + result = fit_nuisance_survival( + data['time'], data['event'].astype(int), data['T'], data['X'], + model_censoring=CoxPHSurvivalAnalysis()) + + pseudo_y = ipcw_cut_rmst( + data['T'], data['time'], data['event'], 4.0, + result.G_a0, result.G_a1, + time_grid=result.time_grid) + + self.assertEqual(pseudo_y.shape, (200,)) + self.assertTrue(np.all(np.isfinite(pseudo_y))) + + def test_nuisance_to_aipcw(self): + from econml.censor import aipcw_cut_rmst + + data = make_survival_data(n=200, tau=4.0, seed=99, + compute_true_cate=False) + result = fit_nuisance_survival( + data['time'], data['event'].astype(int), data['T'], data['X'], + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis()) + + pseudo_y = aipcw_cut_rmst( + data['T'], data['time'], data['event'], 4.0, + result.G_a0, result.G_a1, + result.S_a0, result.S_a1, + time_grid=result.time_grid) + + self.assertEqual(pseudo_y.shape, (200,)) + self.assertTrue(np.all(np.isfinite(pseudo_y))) + + +class TestCrossFitNuisanceSurvival(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_survival_data(n=240, tau=4.0, seed=314, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.time = data['time'] + cls.event = data['event'].astype(int) + cls.tau = 4.0 + + def test_crossfit_shapes_and_weights(self): + result = fit_nuisance_survival_crossfit( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + propensity_model=LogisticRegression(max_iter=1000), + cv=3, + random_state=123) + + self.assertIsInstance(result, CrossFitNuisanceResult) + ns = len(result.time_grid) + self.assertEqual(result.G_a0.shape, (len(self.T), ns)) + self.assertEqual(result.G_a1.shape, (len(self.T), ns)) + self.assertEqual(result.S_a0.shape, (len(self.T), ns)) + self.assertEqual(result.S_a1.shape, (len(self.T), ns)) + self.assertEqual(result.ps.shape, (len(self.T),)) + self.assertTrue(np.all(np.isfinite(result.ps))) + self.assertTrue(np.all(result.ps >= 1e-3)) + self.assertTrue(np.all(result.ps <= 1 - 1e-3)) + self.assertTrue(np.all(np.isfinite(result.iptw))) + self.assertTrue(np.all(np.isfinite(result.ow))) + self.assertTrue(np.all(result.naive == 1.0)) + + def test_crossfit_pipeline_to_aipcw_and_uif(self): + from econml.censor import aipcw_cut_rmst, uif_diff_rmst + + result = fit_nuisance_survival_crossfit( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + propensity_model=LogisticRegression(max_iter=1000), + cv=3, + random_state=123) + + y_aipcw = aipcw_cut_rmst( + self.T, self.time, self.event, self.tau, + result.G_a0, result.G_a1, result.S_a0, result.S_a1, + time_grid=result.time_grid) + y_uif = uif_diff_rmst( + self.T, self.time, self.event, self.tau, + bw=result.iptw, tilt=result.naive, + G_a0=result.G_a0, G_a1=result.G_a1, + S_a0=result.S_a0, S_a1=result.S_a1, + time_grid=result.time_grid) + + self.assertEqual(y_aipcw.shape, (len(self.T),)) + self.assertEqual(y_uif.shape, (len(self.T),)) + self.assertTrue(np.all(np.isfinite(y_aipcw))) + self.assertTrue(np.all(np.isfinite(y_uif))) + + +class TestCrossFitNuisanceCompetingRisks(unittest.TestCase): + + @classmethod + def setUpClass(cls): + data = make_competing_data(n=240, tau=4.0, seed=2718, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.time = data['time'] + cls.event = data['event'].astype(int) + cls.tau = 4.0 + + def test_crossfit_all_matrices_and_propensity(self): + result = fit_nuisance_competing_crossfit( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + model_cause=CoxPHSurvivalAnalysis(), + model_competing=CoxPHSurvivalAnalysis(), + propensity_model=LogisticRegression(max_iter=1000), + cause=1, + cv=3, + random_state=123) + + ns = len(result.time_grid) + for attr in ['G_a0', 'G_a1', 'S_a0', 'S_a1', + 'Sj_a0', 'Sj_a1', 'Sjbar_a0', 'Sjbar_a1']: + mat = getattr(result, attr) + self.assertEqual(mat.shape, (len(self.T), ns)) + self.assertTrue(np.all(np.isfinite(mat))) + self.assertTrue(np.all(np.isfinite(result.ps))) + self.assertTrue(np.all(np.isfinite(result.iptw))) + + def test_crossfit_pipeline_to_competing_aipcw_and_uif(self): + from econml.censor import aipcw_cut_rmtlj, uif_diff_rmtlj + + result = fit_nuisance_competing_crossfit( + self.time, self.event, self.T, self.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + model_cause=CoxPHSurvivalAnalysis(), + propensity_model=LogisticRegression(max_iter=1000), + cause=1, + cv=3, + random_state=123) + + y_aipcw = aipcw_cut_rmtlj( + self.T, self.time, self.event, self.tau, + result.G_a0, result.G_a1, result.S_a0, result.S_a1, + result.Sj_a0, result.Sj_a1, + cause=1, + time_grid=result.time_grid) + y_uif = uif_diff_rmtlj( + self.T, self.time, self.event, self.tau, + bw=result.iptw, tilt=result.naive, + G_a0=result.G_a0, G_a1=result.G_a1, + S_a0=result.S_a0, S_a1=result.S_a1, + Sj_a0=result.Sj_a0, Sj_a1=result.Sj_a1, + cause=1, + time_grid=result.time_grid) + + self.assertEqual(y_aipcw.shape, (len(self.T),)) + self.assertEqual(y_uif.shape, (len(self.T),)) + self.assertTrue(np.all(np.isfinite(y_aipcw))) + self.assertTrue(np.all(np.isfinite(y_uif))) + + +if __name__ == '__main__': + unittest.main() diff --git a/econml/tests/test_censor/test_simulation.py b/econml/tests/test_censor/test_simulation.py new file mode 100644 index 000000000..d6223f875 --- /dev/null +++ b/econml/tests/test_censor/test_simulation.py @@ -0,0 +1,457 @@ +# Copyright (c) PyWhy contributors. All rights reserved. +# Licensed under the MIT License. + +"""End-to-end simulation tests for censored-outcome HTE workflows. + +These tests sit above the smoke/unit tests and exercise the notebook-style +pipelines end to end: + + simulation DGP -> OOS nuisances -> CUT/UIF transforms -> learner fit/predict + +The goal is not exact numerical replication of the original R simulations. +Instead, the tests verify that representative workflows: + + - run without error on the ported DGPs + - return finite out-of-sample training predictions + - retain non-trivial signal relative to the known simulation truth for a + small set of stable estimators +""" + +import unittest + +import numpy as np +from sksurv.ensemble import RandomSurvivalForest +from sksurv.linear_model import CoxPHSurvivalAnalysis + +from econml.censor import ( + fit_nuisance_survival_crossfit, + fit_nuisance_competing_crossfit, + aipcw_cut_rmst, + uif_diff_rmst, + aipcw_cut_rmtlj, + aipcw_cut_rmtlj_sep_direct_astar1, + aipcw_cut_rmtlj_sep_indirect_astar1, + uif_diff_rmtlj_sep_indirect_astar1, +) +from econml.grf import CausalSurvivalForest, GRFCausalForest +from econml.metalearners._censor_metalearners import ( + SurvivalTLearner, + CompetingRisksTLearner, + SeparableDirectAstar1TLearner, + SeparableIndirectAstar1TLearner, + TLearner, + AIPTWLearner, + RLearner, + IFLearner, +) + +from ._helpers import gbr, lr +from .dgp import make_survival_data, make_competing_data + + +def _rsf(seed=0): + return RandomSurvivalForest( + n_estimators=20, + min_samples_leaf=5, + random_state=seed, + ) + + +def _corr(pred, truth): + pred = np.asarray(pred).ravel() + truth = np.asarray(truth).ravel() + return float(np.corrcoef(pred, truth)[0, 1]) + + +class TestEndToEndSurvivalSimulation(unittest.TestCase): + + @classmethod + def setUpClass(cls): + tau = 2.0 + data = make_survival_data(n=220, tau=tau, seed=123, compute_true_cate=True) + + cls.X = data["X"] + cls.T = data["T"] + cls.time = data["time"] + cls.event = data["event"] + cls.Y = data["Y"] + cls.true_cate = data["true_cate"] + cls.n = cls.X.shape[0] + + nuis = fit_nuisance_survival_crossfit( + cls.time, + cls.event.astype(int), + cls.T, + cls.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + propensity_model=lr(), + cv=2, + random_state=123, + ) + cls.nuis = nuis + cls.Y_aipcw = aipcw_cut_rmst( + cls.T, + cls.time, + cls.event, + tau, + nuis.G_a0, + nuis.G_a1, + nuis.S_a0, + nuis.S_a1, + time_grid=nuis.time_grid, + ) + cls.Y_uif = uif_diff_rmst( + cls.T, + cls.time, + cls.event, + tau, + nuis.iptw, + nuis.naive, + nuis.G_a0, + nuis.G_a1, + nuis.S_a0, + nuis.S_a1, + time_grid=nuis.time_grid, + ) + + cls.est_survival_t = SurvivalTLearner( + models=_rsf(0), + tau=tau, + cv=2, + random_state=0, + ) + cls.est_survival_t.fit(cls.Y, cls.T, X=cls.X) + cls.pred_survival_t = cls.est_survival_t.effect(cls.X) + + cls.est_tl = TLearner(model_mu=gbr(), cv=2, random_state=0) + cls.est_tl.fit(cls.Y_aipcw, cls.T, X=cls.X) + cls.pred_tl = cls.est_tl.effect(cls.X) + + cls.est_aiptw = AIPTWLearner( + propensity_model=lr(), + model_mu=gbr(), + model_cate=gbr(), + cv=2, + random_state=0, + ) + cls.est_aiptw.fit(cls.Y_aipcw, cls.T, X=cls.X) + cls.pred_aiptw = cls.est_aiptw.effect(cls.X) + + cls.est_r = RLearner( + propensity_model=lr(), + model_mu=gbr(), + model_cate=gbr(), + cv=2, + random_state=0, + ) + cls.est_r.fit(cls.Y_aipcw, cls.T, X=cls.X) + cls.pred_r = cls.est_r.effect(cls.X) + + cls.est_if = IFLearner( + propensity_model=lr(), + model_cate=gbr(), + cv=2, + random_state=0, + ) + cls.est_if.fit(cls.Y_uif, X=cls.X) + cls.pred_if = cls.est_if.effect(cls.X) + + cls.est_csf = CausalSurvivalForest( + horizon=tau, + n_estimators=52, + random_state=123, + ) + cls.est_csf.fit(cls.X, cls.T, cls.time, cls.event.astype(bool)) + cls.pred_csf = cls.est_csf.effect(cls.X) + + cls.est_cf = GRFCausalForest(num_trees=52, seed=123) + cls.est_cf.fit(cls.X, cls.Y_aipcw, cls.T) + cls.pred_cf = cls.est_cf.predict().predictions + + def test_survival_workflow_outputs_are_finite(self): + self.assertEqual(self.Y_aipcw.shape, (self.n,)) + self.assertEqual(self.Y_uif.shape, (self.n,)) + self.assertGreater(np.std(self.Y_aipcw), 0.0) + self.assertGreater(np.std(self.Y_uif), 0.0) + + for pred in ( + self.pred_survival_t, + self.pred_tl, + self.pred_aiptw, + self.pred_r, + self.pred_if, + self.pred_csf, + self.pred_cf, + ): + self.assertEqual(np.asarray(pred).shape, (self.n,)) + self.assertTrue(np.all(np.isfinite(pred))) + + def test_survival_crossfit_and_oob_contracts_hold(self): + np.testing.assert_allclose( + self.est_survival_t.effect(self.X), + self.est_survival_t._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_tl.effect(self.X), + self.est_tl._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_aiptw.effect(self.X), + self.est_aiptw._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_r.effect(self.X), + self.est_r._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_if.effect(self.X), + self.est_if._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_csf.effect(self.X), + np.asarray(self.est_csf.oob_predict(self.X)).ravel(), + ) + + def test_survival_representative_learners_track_truth(self): + self.assertGreater(_corr(self.pred_survival_t, self.true_cate), 0.15) + self.assertGreater(_corr(self.pred_csf, self.true_cate), 0.15) + + +class TestEndToEndCompetingSimulation(unittest.TestCase): + + @classmethod + def setUpClass(cls): + tau = 4.0 + data = make_competing_data(n=260, tau=tau, seed=456, compute_true_cate=True) + + cls.X = data["X"] + cls.T = data["T"] + cls.time = data["time"] + cls.event = data["event"] + cls.Y = data["Y"] + cls.true_total = data["true_cate_total"] + cls.true_direct = data["true_cate_direct"] + cls.true_indirect = data["true_cate_indirect"] + cls.n = cls.X.shape[0] + + nuis = fit_nuisance_competing_crossfit( + cls.time, + cls.event, + cls.T, + cls.X, + model_censoring=_rsf(1), + model_event=_rsf(2), + model_cause=_rsf(3), + model_competing=_rsf(4), + propensity_model=lr(), + cause=1, + cv=2, + random_state=456, + ) + cls.nuis = nuis + cls.Y_total = aipcw_cut_rmtlj( + cls.T, + cls.time, + cls.event, + tau, + nuis.G_a0, + nuis.G_a1, + nuis.S_a0, + nuis.S_a1, + nuis.Sj_a0, + nuis.Sj_a1, + cause=1, + time_grid=nuis.time_grid, + ) + cls.Y_direct = aipcw_cut_rmtlj_sep_direct_astar1( + cls.T, + cls.time, + cls.event, + tau, + nuis.G_a0, + nuis.G_a1, + nuis.S_a0, + nuis.S_a1, + nuis.Sj_a0, + nuis.Sj_a1, + nuis.Sjbar_a0, + nuis.Sjbar_a1, + cause=1, + time_grid=nuis.time_grid, + ) + cls.Y_indirect = aipcw_cut_rmtlj_sep_indirect_astar1( + cls.T, + cls.time, + cls.event, + tau, + nuis.G_a0, + nuis.G_a1, + nuis.S_a0, + nuis.S_a1, + nuis.Sj_a0, + nuis.Sj_a1, + nuis.Sjbar_a0, + nuis.Sjbar_a1, + cause=1, + time_grid=nuis.time_grid, + ) + cls.Y_uif_indirect = uif_diff_rmtlj_sep_indirect_astar1( + cls.T, + nuis.ps, + cls.time, + cls.event, + tau, + nuis.iptw, + nuis.naive, + nuis.G_a0, + nuis.G_a1, + nuis.S_a0, + nuis.S_a1, + nuis.Sj_a0, + nuis.Sj_a1, + nuis.Sjbar_a0, + nuis.Sjbar_a1, + cause=1, + time_grid=nuis.time_grid, + ) + + cls.est_competing_total = CompetingRisksTLearner( + models=_rsf(5), + models_cause=_rsf(6), + tau=tau, + cv=2, + random_state=0, + ) + cls.est_competing_total.fit(cls.Y, cls.T, X=cls.X) + cls.pred_competing_total = cls.est_competing_total.effect(cls.X) + + cls.est_competing_direct = SeparableDirectAstar1TLearner( + models=_rsf(7), + models_cause=_rsf(8), + models_competing=_rsf(9), + tau=tau, + cv=2, + random_state=0, + ) + cls.est_competing_direct.fit(cls.Y, cls.T, X=cls.X) + cls.pred_competing_direct = cls.est_competing_direct.effect(cls.X) + + cls.est_competing_indirect = SeparableIndirectAstar1TLearner( + models=_rsf(7), + models_cause=_rsf(8), + models_competing=_rsf(9), + tau=tau, + cv=2, + random_state=0, + ) + cls.est_competing_indirect.fit(cls.Y, cls.T, X=cls.X) + cls.pred_competing_indirect = cls.est_competing_indirect.effect(cls.X) + + cls.est_tl_total = TLearner(model_mu=gbr(), cv=2, random_state=0) + cls.est_tl_total.fit(cls.Y_total, cls.T, X=cls.X) + cls.pred_tl_total = cls.est_tl_total.effect(cls.X) + + cls.est_tl_direct = TLearner(model_mu=gbr(), cv=2, random_state=0) + cls.est_tl_direct.fit(cls.Y_direct, cls.T, X=cls.X) + cls.pred_tl_direct = cls.est_tl_direct.effect(cls.X) + + cls.est_aiptw_total = AIPTWLearner( + propensity_model=lr(), + model_mu=gbr(), + model_cate=gbr(), + cv=2, + random_state=0, + ) + cls.est_aiptw_total.fit(cls.Y_total, cls.T, X=cls.X) + cls.pred_aiptw_total = cls.est_aiptw_total.effect(cls.X) + + cls.est_r_total = RLearner( + propensity_model=lr(), + model_mu=gbr(), + model_cate=gbr(), + cv=2, + random_state=0, + ) + cls.est_r_total.fit(cls.Y_total, cls.T, X=cls.X) + cls.pred_r_total = cls.est_r_total.effect(cls.X) + + cls.est_if_indirect = IFLearner( + propensity_model=lr(), + model_cate=gbr(), + cv=2, + random_state=0, + ) + cls.est_if_indirect.fit(cls.Y_uif_indirect, X=cls.X) + cls.pred_if_indirect = cls.est_if_indirect.effect(cls.X) + + cls.est_cf_total = GRFCausalForest(num_trees=52, seed=123) + cls.est_cf_total.fit(cls.X, cls.Y_total, cls.T) + cls.pred_cf_total = cls.est_cf_total.predict().predictions + + def test_competing_workflow_outputs_are_finite(self): + for y in (self.Y_total, self.Y_direct, self.Y_indirect, self.Y_uif_indirect): + self.assertEqual(np.asarray(y).shape, (self.n,)) + self.assertTrue(np.all(np.isfinite(y))) + self.assertGreater(np.std(y), 0.0) + + for pred in ( + self.pred_competing_total, + self.pred_competing_direct, + self.pred_competing_indirect, + self.pred_tl_total, + self.pred_tl_direct, + self.pred_aiptw_total, + self.pred_r_total, + self.pred_if_indirect, + self.pred_cf_total, + ): + self.assertEqual(np.asarray(pred).shape, (self.n,)) + self.assertTrue(np.all(np.isfinite(pred))) + + def test_competing_crossfit_and_oob_contracts_hold(self): + np.testing.assert_allclose( + self.est_competing_total.effect(self.X), + self.est_competing_total._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_competing_direct.effect(self.X), + self.est_competing_direct._training_oof_separable_direct_, + ) + np.testing.assert_allclose( + self.pred_competing_direct, + self.est_competing_direct._training_oof_separable_direct_, + ) + np.testing.assert_allclose( + self.pred_competing_indirect, + self.est_competing_indirect._training_oof_separable_indirect_, + ) + np.testing.assert_allclose( + self.est_tl_total.effect(self.X), + self.est_tl_total._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_tl_direct.effect(self.X), + self.est_tl_direct._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_aiptw_total.effect(self.X), + self.est_aiptw_total._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_r_total.effect(self.X), + self.est_r_total._training_oof_effect_, + ) + np.testing.assert_allclose( + self.est_if_indirect.effect(self.X), + self.est_if_indirect._training_oof_effect_, + ) + + def test_competing_representative_learners_track_truth(self): + self.assertGreater(_corr(self.pred_competing_total, self.true_total), 0.20) + self.assertGreater(_corr(self.pred_cf_total, self.true_total), 0.20) + self.assertGreater(_corr(self.pred_competing_direct, self.true_direct), 0.05) + + +if __name__ == "__main__": + unittest.main() diff --git a/econml/tests/test_censor/test_survival_meta.py b/econml/tests/test_censor/test_survival_meta.py new file mode 100644 index 000000000..075e0f939 --- /dev/null +++ b/econml/tests/test_censor/test_survival_meta.py @@ -0,0 +1,327 @@ +"""Smoke tests for SurvivalTLearner, SurvivalSLearner, and pseudo-outcome learners +(IPTWLearner, ULearner, MCLearner, MCEALearner). + +DGP mirrors original/simulation/survival/survival_hte_simulation_case1.R +(case 1: proportional-hazards-style with log-logistic / log-normal event times). + +Tests verify: + - fit() runs without error + - effect() returns shape (n, 1) + - ate() returns a scalar + - effect values are finite +""" + +import unittest +import numpy as np +from sksurv.ensemble import RandomSurvivalForest + +from econml.metalearners._censor_metalearners import (SurvivalTLearner, SurvivalSLearner, + IPTWLearner, ULearner, MCLearner, MCEALearner, + RALearner, IFLearner) +from econml.censor import fit_nuisance_survival, aipcw_cut_rmst, uif_diff_rmst +from ._helpers import gbr +from .dgp import make_survival_data + + +class TestSurvivalTLearner(unittest.TestCase): + @classmethod + def setUpClass(cls): + data = make_survival_data(n=300, tau=2.0, seed=42, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.Y = data['Y'] + + def _make_model(self): + return RandomSurvivalForest(n_estimators=50, min_samples_leaf=5, + random_state=0) + + def test_fit_effect_shape(self): + est = SurvivalTLearner(models=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + # effect() squeezes d_t=1 dimension → (n,) per EconML convention + self.assertEqual(cate.shape, (self.X.shape[0],)) + + def test_effect_finite(self): + est = SurvivalTLearner(models=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertTrue(np.all(np.isfinite(cate))) + + def test_ate_scalar(self): + est = SurvivalTLearner(models=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + ate = est.ate(self.X) + # ate() returns scalar () when d_t=1 + self.assertEqual(np.asarray(ate).ndim, 0) + self.assertTrue(np.isfinite(ate)) + + def test_const_marginal_effect_shape(self): + est = SurvivalTLearner(models=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cme = est.const_marginal_effect(self.X) + self.assertEqual(cme.shape, (self.X.shape[0], 1)) + + def test_tau_respected(self): + """RMST at tau=0.5 should be <= RMST at tau=2.0 everywhere.""" + est_small = SurvivalTLearner(models=self._make_model(), tau=0.5) + est_large = SurvivalTLearner(models=self._make_model(), tau=2.0) + est_small.fit(self.Y, self.T, X=self.X) + est_large.fit(self.Y, self.T, X=self.X) + # RMST(tau=0.5) ≤ RMST(tau=2) → |CATE| tends to be smaller at smaller tau + # We just check no error and shapes agree + self.assertEqual(est_small.effect(self.X).shape, + est_large.effect(self.X).shape) + + def test_default_survival_model(self): + est = SurvivalTLearner(tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(cate))) + + +class TestSurvivalSLearner(unittest.TestCase): + @classmethod + def setUpClass(cls): + data = make_survival_data(n=300, tau=2.0, seed=42, + compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.Y = data['Y'] + + def _make_model(self): + return RandomSurvivalForest(n_estimators=50, min_samples_leaf=5, + random_state=0) + + def test_fit_effect_shape(self): + est = SurvivalSLearner(overall_model=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + # effect() squeezes d_t=1 dimension → (n,) per EconML convention + self.assertEqual(cate.shape, (self.X.shape[0],)) + + def test_effect_finite(self): + est = SurvivalSLearner(overall_model=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertTrue(np.all(np.isfinite(cate))) + + def test_ate_scalar(self): + est = SurvivalSLearner(overall_model=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + ate = est.ate(self.X) + # ate() returns scalar () when d_t=1 + self.assertEqual(np.asarray(ate).ndim, 0) + self.assertTrue(np.isfinite(ate)) + + def test_const_marginal_effect_shape(self): + est = SurvivalSLearner(overall_model=self._make_model(), tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cme = est.const_marginal_effect(self.X) + self.assertEqual(cme.shape, (self.X.shape[0], 1)) + + def test_default_survival_model(self): + est = SurvivalSLearner(tau=2.0) + est.fit(self.Y, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(cate))) + + +# --------------------------------------------------------------------------- +# Shared fixture for pseudo-outcome learner tests +# --------------------------------------------------------------------------- + +class _PseudoOutcomeBase(unittest.TestCase): + """Base class providing a pre-computed AIPCW pseudo-outcome for testing.""" + + @classmethod + def setUpClass(cls): + from sksurv.linear_model import CoxPHSurvivalAnalysis + data = make_survival_data(n=300, tau=2.0, seed=42, compute_true_cate=False) + cls.X = data['X'] + cls.T = data['T'] + cls.time = data['time'] + cls.event = data['event'] + + cox = CoxPHSurvivalAnalysis() + nuis = fit_nuisance_survival( + cls.time, cls.event, cls.T, cls.X, + model_censoring=cox, + model_event=cox, + ) + cls.Y_star = aipcw_cut_rmst( + cls.T, cls.time, cls.event, 2.0, + nuis.G_a0, nuis.G_a1, nuis.S_a0, nuis.S_a1, + time_grid=nuis.time_grid, + ) + cls.n = cls.X.shape[0] + +class TestIPTWLearner(_PseudoOutcomeBase): + + def test_fit_effect_shape(self): + est = IPTWLearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = IPTWLearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_default_model_cate(self): + est = IPTWLearner() + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + +class TestULearner(_PseudoOutcomeBase): + + def test_fit_effect_shape(self): + est = ULearner(model_cate=gbr(), model_mu=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = ULearner(model_cate=gbr(), model_mu=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_default_model_mu(self): + est = ULearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + def test_default_model_cate(self): + est = ULearner() + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + +class TestMCLearner(_PseudoOutcomeBase): + + def test_fit_effect_shape(self): + est = MCLearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = MCLearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_ate_scalar(self): + est = MCLearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + ate = est.ate(self.X) + self.assertEqual(np.asarray(ate).ndim, 0) + self.assertTrue(np.isfinite(ate)) + + def test_default_model_cate(self): + est = MCLearner() + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + +class TestMCEALearner(_PseudoOutcomeBase): + + def test_fit_effect_shape(self): + est = MCEALearner(model_cate=gbr(), model_mu=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = MCEALearner(model_cate=gbr(), model_mu=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_default_model_mu(self): + est = MCEALearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + def test_default_model_cate(self): + est = MCEALearner() + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + +class TestRALearner(_PseudoOutcomeBase): + + def test_fit_effect_shape(self): + est = RALearner(model_cate=gbr(), model_mu=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + cate = est.effect(self.X) + self.assertEqual(cate.shape, (self.n,)) + + def test_effect_finite(self): + est = RALearner(model_cate=gbr(), model_mu=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.effect(self.X)))) + + def test_default_model_mu(self): + est = RALearner(model_cate=gbr()) + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + def test_default_model_cate(self): + est = RALearner() + est.fit(self.Y_star, self.T, X=self.X) + self.assertEqual(est.effect(self.X).shape, (self.n,)) + + +class TestIFLearner(_PseudoOutcomeBase): + + @classmethod + def setUpClass(cls): + super().setUpClass() + # Compute UIF scores using nuisance already fitted in _PseudoOutcomeBase + from sksurv.linear_model import CoxPHSurvivalAnalysis + nuis = fit_nuisance_survival( + cls.time, cls.event, cls.T, cls.X, + model_censoring=CoxPHSurvivalAnalysis(), + model_event=CoxPHSurvivalAnalysis(), + ) + ps = 0.5 * np.ones_like(cls.T, dtype=float) + bw = 1.0 / (ps * cls.T + (1.0 - ps) * (1.0 - cls.T)) + tilt = np.ones_like(ps) + cls.Y_uif = uif_diff_rmst( + cls.T, cls.time, cls.event, 2.0, + bw, tilt, + nuis.G_a0, nuis.G_a1, + nuis.S_a0, nuis.S_a1, + time_grid=nuis.time_grid, + ) + + def test_fit_effect_shape(self): + est = IFLearner(model_cate=gbr()) + est.fit(self.Y_uif, self.T, X=self.X) + cate = est.const_marginal_effect(self.X) + self.assertEqual(cate.shape, (self.n, 1)) + + def test_effect_finite(self): + est = IFLearner(model_cate=gbr()) + est.fit(self.Y_uif, self.T, X=self.X) + self.assertTrue(np.all(np.isfinite(est.const_marginal_effect(self.X)))) + + def test_ate_scalar(self): + est = IFLearner(model_cate=gbr()) + est.fit(self.Y_uif, self.T, X=self.X) + ate = np.mean(est.const_marginal_effect(self.X)) + self.assertEqual(np.asarray(ate).ndim, 0) + self.assertTrue(np.isfinite(ate)) + + def test_default_model_cate(self): + est = IFLearner() + est.fit(self.Y_uif, self.T, X=self.X) + self.assertEqual(est.const_marginal_effect(self.X).shape, (self.n, 1)) + + +if __name__ == '__main__': + unittest.main() diff --git a/econml/tests/test_censor/test_transformations.py b/econml/tests/test_censor/test_transformations.py new file mode 100644 index 000000000..575adc9b0 --- /dev/null +++ b/econml/tests/test_censor/test_transformations.py @@ -0,0 +1,347 @@ +"""Tests for censoring unbiased transformations (CUT) and UIF transforms. + +Tests verify: + - Helpers produce correct shapes and values + - All 12 transforms run without error and return finite (n,) arrays + - No-censoring sanity check: IPCW with G=1 recovers min(time, tau) + - Double-robustness: AIPCW ≈ IPCW when G is correct +""" + +import unittest +import numpy as np + +from econml.censor._transformations import ( + # helpers + _setup_time_grid, + _clamp_survival, + _incremental_hazard, + _compute_cif_matrix, + _cumulative_integral, + _interpolate_to_tau, + _build_indicators, + _extract_at_time, + # RMST + ipcw_cut_rmst, + bj_cut_rmst, + aipcw_cut_rmst, + uif_diff_rmst, + # RMTL + ipcw_cut_rmtlj, + bj_cut_rmtlj, + aipcw_cut_rmtlj, + aipcw_cut_rmtlj_sep_direct_astar1, + aipcw_cut_rmtlj_sep_indirect_astar1, + uif_diff_rmtlj, + uif_diff_rmtlj_sep_direct_astar1, + uif_diff_rmtlj_sep_indirect_astar1, +) + + +def _make_survival_data(n=200, seed=42): + """Generate simple survival test data with known survival matrices.""" + rng = np.random.RandomState(seed) + + # Covariates not needed for transformation tests — only (a, time, event) + a = rng.binomial(1, 0.5, size=n).astype(float) + time_latent = rng.exponential(5.0, size=n) + cens_time = rng.exponential(8.0, size=n) + admin_cens = 10.0 + obs_time = np.minimum(time_latent, np.minimum(cens_time, admin_cens)) + event = (time_latent <= np.minimum(cens_time, admin_cens)).astype(float) + + # Build a time grid from unique observed times + s = np.sort(np.unique(obs_time)) + ns = len(s) + + # Synthetic survival matrices: S(t) = exp(-0.15 * t) for all subjects + lam = 0.15 + S_mat = np.exp(-lam * s[np.newaxis, :]).repeat(n, axis=0) + # Censoring survival: G(t) = exp(-0.1 * t) + lam_c = 0.1 + G_mat = np.exp(-lam_c * s[np.newaxis, :]).repeat(n, axis=0) + + return dict(a=a, time=obs_time, event=event, s=s, S=S_mat, G=G_mat, + n=n, ns=ns, admin_cens=admin_cens, tau=4.0) + + +def _make_competing_data(n=200, seed=42): + """Generate competing-risk test data with known survival matrices.""" + rng = np.random.RandomState(seed) + + a = rng.binomial(1, 0.5, size=n).astype(float) + # Two cause times + censoring + t_cause1 = rng.exponential(6.0, size=n) + t_cause2 = rng.exponential(8.0, size=n) + cens_time = rng.exponential(10.0, size=n) + admin_cens = 10.0 + + t_event = np.minimum(t_cause1, t_cause2) + obs_time = np.minimum(t_event, np.minimum(cens_time, admin_cens)) + + # Event code: 0=censored, 1=cause1, 2=cause2 + event = np.zeros(n, dtype=int) + not_censored = t_event <= np.minimum(cens_time, admin_cens) + is_cause1 = (t_cause1 <= t_cause2) & not_censored + is_cause2 = (t_cause2 < t_cause1) & not_censored + event[is_cause1] = 1 + event[is_cause2] = 2 + + s = np.sort(np.unique(obs_time)) + ns = len(s) + + # Synthetic matrices + S_mat = np.exp(-0.12 * s[np.newaxis, :]).repeat(n, axis=0) # overall + Sj_mat = np.exp(-0.06 * s[np.newaxis, :]).repeat(n, axis=0) # cause 1 + Sjbar_mat = np.exp(-0.06 * s[np.newaxis, :]).repeat(n, axis=0) # cause 2 + G_mat = np.exp(-0.08 * s[np.newaxis, :]).repeat(n, axis=0) # censoring + + return dict(a=a, time=obs_time, event=event, s=s, + S=S_mat, Sj=Sj_mat, Sjbar=Sjbar_mat, G=G_mat, + n=n, ns=ns, admin_cens=admin_cens, tau=4.0) + + +class TestHelpers(unittest.TestCase): + + def test_setup_time_grid_unique(self): + time = np.array([1.0, 3.0, 2.0, 5.0, 4.0]) + s, ds, ind = _setup_time_grid(time, tau=3.5) + np.testing.assert_array_equal(s, [1, 2, 3, 4, 5]) + np.testing.assert_array_almost_equal(ds, [1, 1, 1, 1, 1]) + self.assertEqual(ind, 2) # s[2]=3 < 3.5 + + def test_setup_time_grid_freq(self): + time = np.array([1.0, 2.0]) + s, ds, ind = _setup_time_grid(time, tau=2.5, time_grid=1.0, admin_cens=5.0) + np.testing.assert_array_almost_equal(s, [1, 2, 3, 4, 5]) + self.assertEqual(ind, 1) # s[1]=2 < 2.5 < s[2]=3 + + def test_clamp_survival(self): + S = np.array([[0.0001, 0.5, np.nan], + [0.9, 0.0, 0.3]]) + result = _clamp_survival(S.copy()) + self.assertTrue(np.all(result >= 1e-3)) + self.assertFalse(np.any(np.isnan(result))) + # NaN at [0,2] should be forward-filled from [0,1]=0.5 + self.assertAlmostEqual(result[0, 2], 0.5) + + def test_incremental_hazard_shape(self): + S = np.exp(-0.1 * np.arange(1, 11))[np.newaxis, :].repeat(5, axis=0) + dH = _incremental_hazard(S) + self.assertEqual(dH.shape, S.shape) + # For constant rate, increments should be approximately equal + np.testing.assert_array_almost_equal(dH[0], 0.1 * np.ones(10), decimal=5) + + def test_compute_cif_matrix(self): + n, ns = 3, 5 + S = np.exp(-0.1 * np.arange(1, ns + 1))[np.newaxis, :].repeat(n, axis=0) + Sj = np.exp(-0.05 * np.arange(1, ns + 1))[np.newaxis, :].repeat(n, axis=0) + dH_j = _incremental_hazard(Sj) + Fj = _compute_cif_matrix(S, dH_j) + self.assertEqual(Fj.shape, (n, ns)) + # CIF should be non-decreasing + self.assertTrue(np.all(np.diff(Fj, axis=1) >= -1e-10)) + + def test_cumulative_integral(self): + vals = np.ones((2, 5)) + ds = np.array([1, 1, 1, 1, 1], dtype=float) + result = _cumulative_integral(vals, ds) + np.testing.assert_array_almost_equal(result[0], [1, 2, 3, 4, 5]) + + def test_interpolate_to_tau(self): + s = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) + matrix = np.array([[10, 20, 30, 40, 50]], dtype=float) + result = _interpolate_to_tau(matrix, s, tau=2.5, ind=1) + self.assertAlmostEqual(result[0], 25.0) + + def test_build_indicators_shape(self): + time = np.array([1.5, 2.5, 3.5]) + event = np.array([1, 0, 2]) + s = np.array([1.0, 2.0, 3.0, 4.0]) + ind = _build_indicators(time, event, s, cause=1) + self.assertEqual(ind['Yt'].shape, (3, 4)) + self.assertEqual(ind['dNjt'].shape, (3, 4)) + self.assertEqual(ind['dNjbart'].shape, (3, 4)) + + def test_extract_at_time(self): + s = np.array([1.0, 2.0, 3.0]) + matrix = np.array([[10, 20, 30], + [40, 50, 60]], dtype=float) + time = np.array([2.0, 3.0]) + result = _extract_at_time(matrix, time, s) + np.testing.assert_array_almost_equal(result, [20, 60]) + + +class TestRMSTTransformations(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.d = _make_survival_data(n=200) + + def test_ipcw_cut_rmst(self): + d = self.d + result = ipcw_cut_rmst(d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_bj_cut_rmst(self): + d = self.d + result = bj_cut_rmst(d['a'], d['time'], d['event'], d['tau'], + d['S'], d['S'], time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_aipcw_cut_rmst(self): + d = self.d + result = aipcw_cut_rmst(d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], d['S'], d['S'], + time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_uif_diff_rmst(self): + d = self.d + n = d['n'] + bw = np.ones(n) + tilt = np.ones(n) + result = uif_diff_rmst(d['a'], d['time'], d['event'], d['tau'], + bw, tilt, d['G'], d['G'], d['S'], d['S'], + time_grid=d['s']) + self.assertEqual(result.shape, (n,)) + self.assertTrue(np.all(np.isfinite(result))) + + +class TestRMTLTransformations(unittest.TestCase): + + @classmethod + def setUpClass(cls): + cls.d = _make_competing_data(n=200) + + def test_ipcw_cut_rmtlj(self): + d = self.d + result = ipcw_cut_rmtlj(d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_bj_cut_rmtlj(self): + d = self.d + result = bj_cut_rmtlj(d['a'], d['time'], d['event'], d['tau'], + d['S'], d['S'], d['Sj'], d['Sj'], + cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_aipcw_cut_rmtlj(self): + d = self.d + result = aipcw_cut_rmtlj(d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], d['S'], d['S'], + d['Sj'], d['Sj'], + cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_aipcw_sep_direct(self): + d = self.d + result = aipcw_cut_rmtlj_sep_direct_astar1( + d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], d['S'], d['S'], + d['Sj'], d['Sj'], d['Sjbar'], d['Sjbar'], + cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_aipcw_sep_indirect(self): + d = self.d + result = aipcw_cut_rmtlj_sep_indirect_astar1( + d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], d['S'], d['S'], + d['Sj'], d['Sj'], d['Sjbar'], d['Sjbar'], + cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (d['n'],)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_uif_diff_rmtlj(self): + d = self.d + n = d['n'] + bw = np.ones(n) + tilt = np.ones(n) + result = uif_diff_rmtlj(d['a'], d['time'], d['event'], d['tau'], + bw, tilt, + d['G'], d['G'], d['S'], d['S'], + d['Sj'], d['Sj'], + cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (n,)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_uif_sep_direct(self): + d = self.d + n = d['n'] + bw = np.ones(n) + tilt = np.ones(n) + ps = 0.5 * np.ones(n) + result = uif_diff_rmtlj_sep_direct_astar1( + d['a'], ps, d['time'], d['event'], d['tau'], + bw, tilt, + d['G'], d['G'], d['S'], d['S'], + d['Sj'], d['Sj'], d['Sjbar'], d['Sjbar'], + cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (n,)) + self.assertTrue(np.all(np.isfinite(result))) + + def test_uif_sep_indirect(self): + d = self.d + n = d['n'] + bw = np.ones(n) + tilt = np.ones(n) + ps = 0.5 * np.ones(n) + result = uif_diff_rmtlj_sep_indirect_astar1( + d['a'], ps, d['time'], d['event'], d['tau'], + bw, tilt, + d['G'], d['G'], d['S'], d['S'], + d['Sj'], d['Sj'], d['Sjbar'], d['Sjbar'], + cause=1, time_grid=d['s']) + self.assertEqual(result.shape, (n,)) + self.assertTrue(np.all(np.isfinite(result))) + + +class TestNoCensoring(unittest.TestCase): + """When G(t) = 1 for all t, IPCW-RMST should return min(time, tau).""" + + def test_ipcw_rmst_no_censoring(self): + n = 100 + rng = np.random.RandomState(123) + time = rng.exponential(3.0, size=n) + event = np.ones(n) # no censoring + a = rng.binomial(1, 0.5, size=n).astype(float) + tau = 4.0 + + s = np.sort(np.unique(time)) + ns = len(s) + G_perfect = np.ones((n, ns)) # no censoring + + result = ipcw_cut_rmst(a, time, event, tau, G_perfect, G_perfect, + time_grid=s) + expected = np.minimum(time, tau) + np.testing.assert_array_almost_equal(result, expected, decimal=2) + + +class TestDoubleRobustness(unittest.TestCase): + """AIPCW with correct G should approximate IPCW.""" + + def test_aipcw_approx_ipcw(self): + d = _make_survival_data(n=300, seed=99) + # Use correct G and some S for AIPCW + ipcw_result = ipcw_cut_rmst(d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], time_grid=d['s']) + aipcw_result = aipcw_cut_rmst(d['a'], d['time'], d['event'], d['tau'], + d['G'], d['G'], d['S'], d['S'], + time_grid=d['s']) + # With correct G, AIPCW augmentation terms should not drastically change result. + # They're not identical due to the augmentation, but should be correlated. + corr = np.corrcoef(ipcw_result, aipcw_result)[0, 1] + self.assertGreater(corr, 0.5) + + +if __name__ == '__main__': + unittest.main() diff --git a/econml/tests/test_grf_causal_forest_translation.py b/econml/tests/test_grf_causal_forest_translation.py new file mode 100644 index 000000000..f09da3de1 --- /dev/null +++ b/econml/tests/test_grf_causal_forest_translation.py @@ -0,0 +1,74 @@ +import unittest + +import numpy as np + +from econml.grf import GRFCausalForest, causal_forest + + +class TestGRFCausalForestTranslation(unittest.TestCase): + + @classmethod + def setUpClass(cls): + rng = np.random.RandomState(123) + n = 240 + X = rng.normal(size=(n, 4)) + W = rng.binomial(1, 1 / (1 + np.exp(-X[:, 0]))) + tau = 0.5 + X[:, 0] + Y = tau * W + 0.3 * X[:, 1] + rng.normal(size=n) + + cls.X = X + cls.W = W + cls.Y = Y + + def _make_est(self, **kwargs): + return GRFCausalForest( + num_trees=52, + seed=123, + **kwargs, + ) + + def test_class_api_fit_predict(self): + est = self._make_est() + est.fit(self.X, self.Y, self.W) + pred = est.predict(self.X[:12]).predictions + self.assertEqual(pred.shape, (12,)) + self.assertTrue(np.all(np.isfinite(pred))) + + def test_function_api_matches_original_shape(self): + forest = causal_forest(self.X, self.Y, self.W, num_trees=52, seed=123) + pred = forest.predict(self.X[:15]).predictions + self.assertEqual(pred.shape, (15,)) + self.assertTrue(np.all(np.isfinite(pred))) + + def test_predict_without_newdata_returns_oos_training_predictions(self): + est = self._make_est() + est.fit(self.X, self.Y, self.W) + pred = est.predict().predictions + self.assertEqual(pred.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(pred))) + np.testing.assert_allclose(pred, est.effect()) + + def test_nuisances_are_stored(self): + est = self._make_est() + est.fit(self.X, self.Y, self.W) + self.assertEqual(est.Y_hat_.shape, (self.X.shape[0],)) + self.assertEqual(est.W_hat_.shape, (self.X.shape[0],)) + self.assertTrue(np.all(np.isfinite(est.Y_hat_))) + self.assertTrue(np.all(np.isfinite(est.W_hat_))) + + def test_user_supplied_nuisances_are_used(self): + y_hat = np.repeat(np.mean(self.Y), self.X.shape[0]) + w_hat = np.repeat(np.mean(self.W), self.X.shape[0]) + est = self._make_est() + est.fit(self.X, self.Y, self.W, Y_hat=y_hat, W_hat=w_hat) + np.testing.assert_allclose(est.Y_hat_, y_hat) + np.testing.assert_allclose(est.W_hat_, w_hat) + + def test_unsupported_honesty_fraction_raises(self): + est = self._make_est(honesty_fraction=0.75) + with self.assertRaises(NotImplementedError): + est.fit(self.X, self.Y, self.W) + + +if __name__ == "__main__": + unittest.main() diff --git a/notebooks/Competing Risks HTE Examples.ipynb b/notebooks/Competing Risks HTE Examples.ipynb new file mode 100644 index 000000000..c9b58fcf8 --- /dev/null +++ b/notebooks/Competing Risks HTE Examples.ipynb @@ -0,0 +1,1159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "z7zhiyuoyur", + "metadata": {}, + "source": [ + "# Competing Risks HTE Examples: Heterogeneous Treatment Effects with Multiple Causes of Failure\n", + "\n", + "This notebook demonstrates CATE estimation when subjects can experience one of multiple competing events (e.g. cause 1 = relapse, cause 2 = death without relapse). The estimand is the difference in **Restricted Mean Time Lost (RMTL)** for cause j:\n", + "\n", + "$$\\text{CATE}(X) = \\text{RMTL}_j(a\\!=\\!1 \\mid X, \\tau) - \\text{RMTL}_j(a\\!=\\!0 \\mid X, \\tau), \\quad \\text{RMTL}_j = \\int_0^\\tau F_j(t) \\, dt$$\n", + "\n", + "Two approaches:\n", + "1. **Approach A — Direct competing risk metalearners**: Fit cause-specific survival models on raw `(event, time)` data\n", + "2. **Approach B — RMTL CUT transforms + generic learners**: Transform censored competing-risk outcomes into scalar pseudo-outcomes, then use any standard CATE estimator\n", + "\n", + "The DGP mirrors `original/simulation/competing/competing_hte_simulation_case1.R`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "xdk582qeh0o", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# EconML imports\n", + "from econml.metalearners._censor_metalearners import (\n", + " TLearner, SLearner, XLearner,\n", + " CompetingRisksTLearner, CompetingRisksSLearner,\n", + " SeparableDirectAstar1TLearner, SeparableIndirectAstar1TLearner,\n", + " SeparableDirectAstar1SLearner, SeparableIndirectAstar1SLearner,\n", + " IPTWLearner, AIPTWLearner, MCLearner, MCEALearner,\n", + " ULearner, RALearner, RLearner, IFLearner,\n", + ")\n", + "from econml.grf import GRFCausalForest as CausalForest\n", + "from econml.censor import (\n", + " fit_nuisance_competing_crossfit,\n", + " ipcw_cut_rmtlj, bj_cut_rmtlj, aipcw_cut_rmtlj,\n", + " aipcw_cut_rmtlj_sep_direct_astar1, aipcw_cut_rmtlj_sep_indirect_astar1,\n", + " uif_diff_rmtlj,\n", + " uif_diff_rmtlj_sep_direct_astar1, uif_diff_rmtlj_sep_indirect_astar1,\n", + ")\n", + "\n", + "# DGP (ported from R simulation)\n", + "from econml.tests.test_censor.dgp import make_competing_data\n", + "\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "markdown", + "id": "daiwgdwjpc", + "metadata": {}, + "source": [ + "## 1. Data Generating Process\n", + "\n", + "Competing risks DGP with:\n", + "- 6 covariates (3 correlated continuous, 3 binary)\n", + "- Logistic propensity score (same as survival DGP)\n", + "- Two exponential cause times with covariate-dependent rates\n", + "- Treatment-dependent censoring\n", + "- Event codes: 0 = censored, 1 = cause 1 (primary), 2 = cause 2 (competing)\n", + "- True CATE via Monte Carlo: total, separable direct, separable indirect" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "vptl1zwtcu", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n=4000, d=6, tau=4.0\n", + "Treatment rate: 0.49\n", + "Event rates — censored: 0.10, cause 1: 0.48, cause 2: 0.42\n", + "True ATE (total): 0.1315\n", + "True ATE (direct): 0.0958\n", + "True ATE (indirect): 0.0357\n" + ] + } + ], + "source": [ + "tau = 4.0\n", + "data = make_competing_data(n=4000, tau=tau, seed=42, compute_true_cate=True)\n", + "\n", + "X = data['X']\n", + "T = data['T']\n", + "time = data['time']\n", + "event = data['event'] # 0=censored, 1=cause1, 2=cause2\n", + "competing_obj = data['Y'] # structured array for competing risk learners\n", + "true_cate_total = data['true_cate_total']\n", + "true_cate_direct = data['true_cate_direct']\n", + "true_cate_indirect = data['true_cate_indirect']\n", + "\n", + "clip_hte = lambda y: np.clip(np.asarray(y, dtype=float).ravel(), -2 * tau, 2 * tau)\n", + "\n", + "n = len(T)\n", + "print(f\"n={n}, d={X.shape[1]}, tau={tau}\")\n", + "print(f\"Treatment rate: {T.mean():.2f}\")\n", + "print(f\"Event rates — censored: {(event==0).mean():.2f}, cause 1: {(event==1).mean():.2f}, cause 2: {(event==2).mean():.2f}\")\n", + "print(f\"True ATE (total): {true_cate_total.mean():.4f}\")\n", + "print(f\"True ATE (direct): {true_cate_direct.mean():.4f}\")\n", + "print(f\"True ATE (indirect): {true_cate_indirect.mean():.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "7ah3of8sn7o", + "metadata": {}, + "source": [ + "## 2. Approach A — Direct Competing Risk Metalearners\n", + "\n", + "These take raw `(event, time)` structured arrays with multi-cause event codes. They fit overall survival S(t), cause-j survival S_j(t), and optionally competing S_jbar(t) internally.\n", + "\n", + "### 2a. Total effect (RMTL difference)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "o1ynun2rv1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CompetingRisksSLearner ATE: 0.1286\n", + "CompetingRisksTLearner ATE: 0.1207\n" + ] + } + ], + "source": [ + "# CompetingRisksSLearner (cross-fitted): pooled models with [a, X, a*X] features\n", + "cr_s = CompetingRisksSLearner(\n", + " tau=tau,\n", + ")\n", + "cr_s.fit(competing_obj, T, X=X)\n", + "cate_cr_s = clip_hte(cr_s.effect(X))\n", + "\n", + "# CompetingRisksTLearner (cross-fitted): separate models per arm\n", + "cr_t = CompetingRisksTLearner(\n", + " tau=tau,\n", + ")\n", + "cr_t.fit(competing_obj, T, X=X)\n", + "cate_cr_t = clip_hte(cr_t.effect(X))\n", + "\n", + "print(f\"CompetingRisksSLearner ATE: {cate_cr_s.mean():.4f}\")\n", + "print(f\"CompetingRisksTLearner ATE: {cate_cr_t.mean():.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "lk6kpt556le", + "metadata": {}, + "source": [ + "### 2b. Separable effects (direct + indirect)\n", + "\n", + "Decompose the total effect into:\n", + "- **Direct**: effect of treatment on cause j holding competing hazard at reference (a*=1)\n", + "- **Indirect**: effect via competing cause pathway\n", + "\n", + "Requires an additional `models_competing` for the competing cause survival." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "qvxexyvhoxb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Direct ATE Indirect ATE (true: 0.0958 / 0.0357)\n", + "SeparableAstar1SLearners 0.0868 0.0344\n", + "SeparableAstar1TLearners 0.0780 0.0442\n", + "Sum check (S): 0.1212 (T): 0.1222 (total: 0.1315)\n" + ] + } + ], + "source": [ + "# CompetingRisksSLearner with separable effects (cross-fitted)\n", + "cr_s_sep_direct = SeparableDirectAstar1SLearner(\n", + " tau=tau,\n", + ")\n", + "cr_s_sep_direct.fit(competing_obj, T, X=X)\n", + "direct_astar1_cr_s = clip_hte(cr_s_sep_direct.effect(X))\n", + "\n", + "cr_s_sep_indirect = SeparableIndirectAstar1SLearner(\n", + " tau=tau,\n", + ")\n", + "cr_s_sep_indirect.fit(competing_obj, T, X=X)\n", + "indirect_astar1_cr_s = clip_hte(cr_s_sep_indirect.effect(X))\n", + "\n", + "# CompetingRisksTLearner with separable effects (cross-fitted)\n", + "cr_t_sep_direct = SeparableDirectAstar1TLearner(\n", + " tau=tau,\n", + ")\n", + "cr_t_sep_direct.fit(competing_obj, T, X=X)\n", + "direct_astar1_cr_t = clip_hte(cr_t_sep_direct.effect(X))\n", + "\n", + "cr_t_sep_indirect = SeparableIndirectAstar1TLearner(\n", + " tau=tau,\n", + ")\n", + "cr_t_sep_indirect.fit(competing_obj, T, X=X)\n", + "indirect_astar1_cr_t = clip_hte(cr_t_sep_indirect.effect(X))\n", + "\n", + "print(f\"{'':30s} {'Direct ATE':>12s} {'Indirect ATE':>14s} (true: {true_cate_direct.mean():.4f} / {true_cate_indirect.mean():.4f})\")\n", + "print(f\"{'SeparableAstar1SLearners':30s} {direct_astar1_cr_s.mean():12.4f} {indirect_astar1_cr_s.mean():14.4f}\")\n", + "print(f\"{'SeparableAstar1TLearners':30s} {direct_astar1_cr_t.mean():12.4f} {indirect_astar1_cr_t.mean():14.4f}\")\n", + "print(f\"Sum check (S): {(direct_astar1_cr_s + indirect_astar1_cr_s).mean():.4f} (T): {(direct_astar1_cr_t + indirect_astar1_cr_t).mean():.4f} (total: {true_cate_total.mean():.4f})\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "fqhcawsakrt", + "metadata": {}, + "source": [ + "## 3. Approach B — RMTL CUT Transforms + Generic Learners\n", + "\n", + "### 3a. Cross-fit nuisance models and apply RMTL transforms\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "as5109watct", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time grid: 767 points\n", + "Matrices fitted: G=True, S=True, Sj=True, Sjbar=True\n", + "ps range: [0.0431, 0.9531]\n" + ] + } + ], + "source": [ + "# Cross-fit all 4 nuisance model types: G, S, Sj, Sjbar, plus ps(X)\n", + "nuis = fit_nuisance_competing_crossfit(\n", + " time, event, T, X,\n", + " cause=1,\n", + " cv=2,\n", + " random_state=42)\n", + "\n", + "print(f\"Time grid: {len(nuis.time_grid)} points\")\n", + "print(f\"Matrices fitted: G={nuis.G_a0 is not None}, S={nuis.S_a0 is not None}, \"\n", + " f\"Sj={nuis.Sj_a0 is not None}, Sjbar={nuis.Sjbar_a0 is not None}\")\n", + "print(f\"ps range: [{nuis.ps.min():.4f}, {nuis.ps.max():.4f}]\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f61c2dzbv8o", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IPCW RMTL pseudo-outcome: mean=0.9599\n", + "BJ RMTL pseudo-outcome: mean=0.9598\n", + "AIPCW RMTL pseudo-outcome: mean=0.9583\n", + "BJ separable direct pseudo-outcome: mean=1.3685\n", + "BJ separable indirect pseudo-outcome: mean=0.9230\n", + "AIPCW separable direct pseudo-outcome: mean=1.3752\n", + "AIPCW separable indirect pseudo-outcome: mean=0.9233\n" + ] + } + ], + "source": [ + "# RMTL CUT transforms for total effect\n", + "tg = nuis.time_grid\n", + "\n", + "Y_ipcw_total = ipcw_cut_rmtlj(T, time, event, tau, nuis.G_a0, nuis.G_a1,\n", + " cause=1, time_grid=tg)\n", + "\n", + "Y_bj_total = bj_cut_rmtlj(T, time, event, tau, nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1, cause=1, time_grid=tg)\n", + "\n", + "Y_aipcw_total = aipcw_cut_rmtlj(T, time, event, tau,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " cause=1, time_grid=tg)\n", + "\n", + "print(f\"IPCW RMTL pseudo-outcome: mean={Y_ipcw_total.mean():.4f}\")\n", + "print(f\"BJ RMTL pseudo-outcome: mean={Y_bj_total.mean():.4f}\")\n", + "print(f\"AIPCW RMTL pseudo-outcome: mean={Y_aipcw_total.mean():.4f}\")\n", + "\n", + "# RMTL CUT transforms for separable effects (IPCW not available for separable effects)\n", + "n_t = nuis.G_a0.shape[0]\n", + "n_times = nuis.G_a0.shape[1]\n", + "G_ones = np.ones((n_t, n_times))\n", + "\n", + "# BJ separable pseudo-outcomes: singly robust (G=1 identity)\n", + "Y_bj_sep_direct_astar1 = aipcw_cut_rmtlj_sep_direct_astar1(\n", + " T, time, event, tau,\n", + " G_ones, G_ones, nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1, nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg)\n", + "\n", + "Y_bj_sep_indirect_astar1 = aipcw_cut_rmtlj_sep_indirect_astar1(\n", + " T, time, event, tau,\n", + " G_ones, G_ones, nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1, nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg)\n", + "\n", + "# AIPCW separable pseudo-outcomes: doubly robust (recommended)\n", + "Y_aipcw_sep_direct_astar1 = aipcw_cut_rmtlj_sep_direct_astar1(\n", + " T, time, event, tau,\n", + " nuis.G_a0, nuis.G_a1, nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1, nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg)\n", + "\n", + "Y_aipcw_sep_indirect_astar1 = aipcw_cut_rmtlj_sep_indirect_astar1(\n", + " T, time, event, tau,\n", + " nuis.G_a0, nuis.G_a1, nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1, nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg)\n", + "\n", + "print(f\"BJ separable direct pseudo-outcome: mean={Y_bj_sep_direct_astar1.mean():.4f}\")\n", + "print(f\"BJ separable indirect pseudo-outcome: mean={Y_bj_sep_indirect_astar1.mean():.4f}\")\n", + "print(f\"AIPCW separable direct pseudo-outcome: mean={Y_aipcw_sep_direct_astar1.mean():.4f}\")\n", + "print(f\"AIPCW separable indirect pseudo-outcome: mean={Y_aipcw_sep_indirect_astar1.mean():.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "v3qrk7pvg6s", + "metadata": {}, + "source": [ + "### 3b. Feed RMTL pseudo-outcomes into generic learners\n", + "\n", + "After the CUT step, `Y_star` is a plain float array. Any existing EconML estimator works directly.\n", + "\n", + "**Total effect** (`Y_aipcw_total`, doubly robust — recommended):\n", + "- `Y_ipcw_total` and `Y_bj_total` are also available as singly-robust alternatives.\n", + "\n", + "**Separable effects** (`Y_aipcw_sep_direct_astar1`, `Y_aipcw_sep_indirect_astar1`, doubly robust — recommended):\n", + "- `Y_bj_sep_direct_astar1` and `Y_bj_sep_indirect_astar1` are available as singly-robust alternatives." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5maef2l46bk", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ATEs (total effect):\n", + " AIPCW + TLearner : 0.1028\n", + " AIPCW + SLearner : 0.1543\n", + " AIPCW + CausalForest : 0.1205\n", + "ATEs (separable direct, true=0.0958):\n", + " AIPCW sep direct + TLearner : 0.0250\n", + " AIPCW sep direct + SLearner : 0.1212\n", + " AIPCW sep direct + CausalForest : -0.0185\n", + "ATEs (separable indirect, true=0.0357):\n", + " AIPCW sep indirect + TLearner : 0.0380\n", + " AIPCW sep indirect + SLearner : 0.0739\n", + " AIPCW sep indirect + CausalForest : 0.0875\n" + ] + } + ], + "source": [ + "results = {}\n", + "\n", + "# --- Total effect (Y_aipcw_total) ---\n", + "t_est = TLearner(cv=2)\n", + "t_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + TLearner'] = clip_hte(t_est.effect(X))\n", + "\n", + "s_est = SLearner(cv=2)\n", + "s_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + SLearner'] = clip_hte(s_est.effect(X))\n", + "\n", + "cf_est = CausalForest(num_trees=200, seed=42)\n", + "cf_est.fit(X, Y_aipcw_total, T)\n", + "results['AIPCW + CausalForest'] = clip_hte(cf_est.effect(X))\n", + "\n", + "print(\"ATEs (total effect):\")\n", + "for k in ['AIPCW + TLearner', 'AIPCW + SLearner', 'AIPCW + CausalForest']:\n", + " print(f\" {k:40s}: {results[k].mean():.4f}\")\n", + "\n", + "# --- Separable direct effect (Y_aipcw_sep_direct_astar1) ---\n", + "t_sep_d = TLearner(cv=2)\n", + "t_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + TLearner'] = clip_hte(t_sep_d.effect(X))\n", + "\n", + "s_sep_d = SLearner(cv=2)\n", + "s_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + SLearner'] = clip_hte(s_sep_d.effect(X))\n", + "\n", + "cf_sep_d = CausalForest(num_trees=200, seed=42)\n", + "cf_sep_d.fit(X, Y_aipcw_sep_direct_astar1, T)\n", + "results['AIPCW sep direct + CausalForest'] = clip_hte(cf_sep_d.effect(X))\n", + "\n", + "print(\"ATEs (separable direct, true={:.4f}):\".format(true_cate_direct.mean()))\n", + "for k in ['AIPCW sep direct + TLearner', 'AIPCW sep direct + SLearner',\n", + " 'AIPCW sep direct + CausalForest']:\n", + " print(f\" {k:40s}: {results[k].mean():.4f}\")\n", + "\n", + "# --- Separable indirect effect (Y_aipcw_sep_indirect_astar1) ---\n", + "t_sep_i = TLearner(cv=2)\n", + "t_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + TLearner'] = clip_hte(t_sep_i.effect(X))\n", + "\n", + "s_sep_i = SLearner(cv=2)\n", + "s_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + SLearner'] = clip_hte(s_sep_i.effect(X))\n", + "\n", + "cf_sep_i = CausalForest(num_trees=200, seed=42)\n", + "cf_sep_i.fit(X, Y_aipcw_sep_indirect_astar1, T)\n", + "results['AIPCW sep indirect + CausalForest'] = clip_hte(cf_sep_i.effect(X))\n", + "\n", + "print(\"ATEs (separable indirect, true={:.4f}):\".format(true_cate_indirect.mean()))\n", + "for k in ['AIPCW sep indirect + TLearner', 'AIPCW sep indirect + SLearner',\n", + " 'AIPCW sep indirect + CausalForest']:\n", + " print(f\" {k:40s}: {results[k].mean():.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1w5mmdmqv6n", + "metadata": {}, + "source": [ + "### 3c. IFLearner (Uncentered Influence Function)\n", + "\n", + "The UIF transforms produce per-subject pseudo-outcomes:\n", + "- `uif_diff_rmtlj` → total RMTL effect\n", + "- `uif_diff_rmtlj_sep_direct_astar1` → separable direct effect\n", + "- `uif_diff_rmtlj_sep_indirect_astar1` → separable indirect effect\n", + "\n", + "`IFLearner` regresses each UIF score vector on X to estimate CATE(X) — mirroring `uif_learner()` from `hte_learners.R`.\n", + "Here the balancing, tilting, and separable-effect propensity inputs all come from the cross-fitted nuisance step.\n", + "The raw UIF scores (`IF`) are also kept as a reference (population-level, no second-stage regression).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cwc4i5ggcy8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IFLearner ATE (total): 0.1531 (true: 0.1315)\n", + "IFLearner ATE (sep direct): -0.3350 (true: 0.0958)\n", + "IFLearner ATE (sep indirect): 0.4380 (true: 0.0357)\n", + "Raw IF ATE (total): 0.0693\n", + "Raw IF ATE (sep direct): -0.3278\n", + "Raw IF ATE (sep indirect): 0.3971\n" + ] + } + ], + "source": [ + "# UIF: use cross-fitted balancing weights, tilting weights, and propensity scores\n", + "ps = nuis.ps\n", + "bw = nuis.iptw\n", + "tilt = nuis.naive\n", + "\n", + "# --- Total effect ---\n", + "Y_uif_diff_total = uif_diff_rmtlj(T, time, event, tau, bw, tilt,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " cause=1, time_grid=tg)\n", + "if_total = IFLearner(cv=2)\n", + "if_total.fit(Y_uif_diff_total, X=X)\n", + "results['IFLearner (total)'] = clip_hte(if_total.effect(X))\n", + "raw_if_total = Y_uif_diff_total\n", + "\n", + "# --- Separable direct effect ---\n", + "Y_uif_diff_sep_direct_astar1 = uif_diff_rmtlj_sep_direct_astar1(\n", + " T, ps, time, event, tau, bw, tilt,\n", + " nuis.G_a0, nuis.G_a1, nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1, nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg)\n", + "if_sep_d = IFLearner(cv=2)\n", + "if_sep_d.fit(Y_uif_diff_sep_direct_astar1, X=X)\n", + "results['IFLearner (sep direct)'] = clip_hte(if_sep_d.effect(X))\n", + "raw_if_sep_direct = Y_uif_diff_sep_direct_astar1\n", + "\n", + "# --- Separable indirect effect ---\n", + "Y_uif_diff_sep_indirect_astar1 = uif_diff_rmtlj_sep_indirect_astar1(\n", + " T, ps, time, event, tau, bw, tilt,\n", + " nuis.G_a0, nuis.G_a1, nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1, nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg)\n", + "if_sep_i = IFLearner(cv=2)\n", + "if_sep_i.fit(Y_uif_diff_sep_indirect_astar1, X=X)\n", + "results['IFLearner (sep indirect)'] = clip_hte(if_sep_i.effect(X))\n", + "raw_if_sep_indirect = Y_uif_diff_sep_indirect_astar1\n", + "\n", + "print(f\"IFLearner ATE (total): {results['IFLearner (total)'].mean():.4f} (true: {true_cate_total.mean():.4f})\")\n", + "print(f\"IFLearner ATE (sep direct): {results['IFLearner (sep direct)'].mean():.4f} (true: {true_cate_direct.mean():.4f})\")\n", + "print(f\"IFLearner ATE (sep indirect): {results['IFLearner (sep indirect)'].mean():.4f} (true: {true_cate_indirect.mean():.4f})\")\n", + "print(f\"Raw IF ATE (total): {raw_if_total.mean():.4f}\")\n", + "print(f\"Raw IF ATE (sep direct): {raw_if_sep_direct.mean():.4f}\")\n", + "print(f\"Raw IF ATE (sep indirect): {raw_if_sep_indirect.mean():.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "jw4i5tvl9a", + "metadata": {}, + "source": [ + "## 4. Approach C — CUT Transforms + Outcome-Transform Learners\n", + "\n", + "XLearner, AIPTWLearner, RLearner, IPTW, MC, MCEA, U-learner build their own internal pseudo-outcomes on top of the AIPCW RMTL pseudo-outcome `Y*`.\n", + "\n", + "### 4a total effect" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "l59gan670ng", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Approach C ATEs:\n", + " AIPCW + XLearner : 0.1227\n", + " AIPCW + AIPTWLearner : 0.0830\n", + " AIPCW + RLearner : 0.0941\n", + " AIPCW + IPTWLearner : 0.1037\n", + " AIPCW + MCLearner : 0.1407\n", + " AIPCW + MCEALearner : 0.1218\n", + " AIPCW + ULearner : 0.0849\n", + " AIPCW + RALearner : 0.1225\n" + ] + } + ], + "source": [ + "# XLearner (cross-fitted; ≈ x_learner): propensity-weighted cross-residuals\n", + "x_est = XLearner(cv=2)\n", + "x_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + XLearner'] = clip_hte(x_est.effect(X))\n", + "\n", + "# AIPTWLearner (≈ aiptw_learner): doubly robust pseudo-outcome with cross-fitting\n", + "aiptw_est = AIPTWLearner(cv=2)\n", + "aiptw_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + AIPTWLearner'] = clip_hte(aiptw_est.effect(X))\n", + "\n", + "# RLearner (≈ r_learner): weighted U-style regression with cross-fitting\n", + "r_est = RLearner(cv=2)\n", + "r_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + RLearner'] = clip_hte(r_est.effect(X))\n", + "\n", + "# IPTWLearner (cross-fitted; ≈ iptw_learner): phi = Y* * (A/e - (1-A)/(1-e)), regress on X\n", + "iptw_est = IPTWLearner(cv=2)\n", + "iptw_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + IPTWLearner'] = clip_hte(iptw_est.effect(X))\n", + "\n", + "# MCLearner (cross-fitted; ≈ mc_learner): phi = 2*(2A-1)*Y*, propensity-weighted regression\n", + "mc_est = MCLearner(cv=2)\n", + "mc_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + MCLearner'] = clip_hte(mc_est.effect(X))\n", + "\n", + "# MCEALearner (cross-fitted; ≈ mcea_learner): residual-augmented MC, propensity-weighted regression\n", + "mcea_est = MCEALearner(cv=2)\n", + "mcea_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + MCEALearner'] = clip_hte(mcea_est.effect(X))\n", + "\n", + "# ULearner (cross-fitted; ≈ u_learner): phi = (Y* - e*mu1 - (1-e)*mu0) / (A - e), regress on X\n", + "u_est = ULearner(cv=2)\n", + "u_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + ULearner'] = clip_hte(u_est.effect(X))\n", + "\n", + "# RALearner (cross-fitted; ≈ ra_learner): phi = A*(Y* - mu0) + (1-A)*(mu1 - Y*), regress on X\n", + "ra_est = RALearner(cv=2)\n", + "ra_est.fit(Y_aipcw_total, T, X=X)\n", + "results['AIPCW + RALearner'] = clip_hte(ra_est.effect(X))\n", + "\n", + "print(\"Approach C ATEs:\")\n", + "for k in ['AIPCW + XLearner', 'AIPCW + AIPTWLearner', 'AIPCW + RLearner',\n", + " 'AIPCW + IPTWLearner', 'AIPCW + MCLearner',\n", + " 'AIPCW + MCEALearner', 'AIPCW + ULearner', 'AIPCW + RALearner']:\n", + " print(f\" {k:30s}: {results[k].mean():.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "sugozn0lbt", + "metadata": {}, + "source": [ + "### 4b separable direct effect" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "oet1df9dcee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ATEs (separable direct, true=0.0958):\n", + " AIPCW sep direct + XLearner : -0.0254\n", + " AIPCW sep direct + AIPTWLearner : -0.0140\n", + " AIPCW sep direct + RLearner : 0.0753\n", + " AIPCW sep direct + IPTWLearner : 0.1133\n", + " AIPCW sep direct + MCLearner : 0.0653\n", + " AIPCW sep direct + MCEALearner : -0.0473\n", + " AIPCW sep direct + ULearner : 0.0938\n", + " AIPCW sep direct + RALearner : -0.0151\n" + ] + } + ], + "source": [ + "# XLearner (cross-fitted)\n", + "x_sep_d = XLearner(cv=2)\n", + "x_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + XLearner'] = clip_hte(x_sep_d.effect(X))\n", + "\n", + "# AIPTWLearner\n", + "aiptw_sep_d = AIPTWLearner(cv=2)\n", + "aiptw_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + AIPTWLearner'] = clip_hte(aiptw_sep_d.effect(X))\n", + "\n", + "# RLearner\n", + "r_sep_d = RLearner(cv=2)\n", + "r_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + RLearner'] = clip_hte(r_sep_d.effect(X))\n", + "\n", + "# IPTWLearner (cross-fitted)\n", + "iptw_sep_d = IPTWLearner(cv=2)\n", + "iptw_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + IPTWLearner'] = clip_hte(iptw_sep_d.effect(X))\n", + "\n", + "# MCLearner (cross-fitted)\n", + "mc_sep_d = MCLearner(cv=2)\n", + "mc_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + MCLearner'] = clip_hte(mc_sep_d.effect(X))\n", + "\n", + "# MCEALearner (cross-fitted)\n", + "mcea_sep_d = MCEALearner(cv=2)\n", + "mcea_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + MCEALearner'] = clip_hte(mcea_sep_d.effect(X))\n", + "\n", + "# ULearner (cross-fitted)\n", + "u_sep_d = ULearner(cv=2)\n", + "u_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + ULearner'] = clip_hte(u_sep_d.effect(X))\n", + "\n", + "# RALearner (cross-fitted)\n", + "ra_sep_d = RALearner(cv=2)\n", + "ra_sep_d.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results['AIPCW sep direct + RALearner'] = clip_hte(ra_sep_d.effect(X))\n", + "\n", + "print(f\"ATEs (separable direct, true={true_cate_direct.mean():.4f}):\")\n", + "for k in ['AIPCW sep direct + XLearner', 'AIPCW sep direct + AIPTWLearner',\n", + " 'AIPCW sep direct + RLearner', 'AIPCW sep direct + IPTWLearner',\n", + " 'AIPCW sep direct + MCLearner', 'AIPCW sep direct + MCEALearner',\n", + " 'AIPCW sep direct + ULearner', 'AIPCW sep direct + RALearner']:\n", + " print(f\" {k:40s}: {results[k].mean():.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7742e030", + "metadata": {}, + "source": [ + "### 4c separable indirect effect" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "lf5jscs30v", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ATEs (separable indirect, true=0.0357):\n", + " AIPCW sep indirect + XLearner : 0.0702\n", + " AIPCW sep indirect + AIPTWLearner : 0.0529\n", + " AIPCW sep indirect + RLearner : 0.0891\n", + " AIPCW sep indirect + IPTWLearner : -0.0058\n", + " AIPCW sep indirect + MCLearner : -0.0257\n", + " AIPCW sep indirect + MCEALearner : 0.0462\n", + " AIPCW sep indirect + ULearner : 0.0785\n", + " AIPCW sep indirect + RALearner : 0.0615\n" + ] + } + ], + "source": [ + "# XLearner (cross-fitted)\n", + "x_sep_i = XLearner(cv=2)\n", + "x_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + XLearner'] = clip_hte(x_sep_i.effect(X))\n", + "\n", + "# AIPTWLearner\n", + "aiptw_sep_i = AIPTWLearner(cv=2)\n", + "aiptw_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + AIPTWLearner'] = clip_hte(aiptw_sep_i.effect(X))\n", + "\n", + "# RLearner\n", + "r_sep_i = RLearner(cv=2)\n", + "r_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + RLearner'] = clip_hte(r_sep_i.effect(X))\n", + "\n", + "# IPTWLearner (cross-fitted)\n", + "iptw_sep_i = IPTWLearner(cv=2)\n", + "iptw_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + IPTWLearner'] = clip_hte(iptw_sep_i.effect(X))\n", + "\n", + "# MCLearner (cross-fitted)\n", + "mc_sep_i = MCLearner(cv=2)\n", + "mc_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + MCLearner'] = clip_hte(mc_sep_i.effect(X))\n", + "\n", + "# MCEALearner (cross-fitted)\n", + "mcea_sep_i = MCEALearner(cv=2)\n", + "mcea_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + MCEALearner'] = clip_hte(mcea_sep_i.effect(X))\n", + "\n", + "# ULearner (cross-fitted)\n", + "u_sep_i = ULearner(cv=2)\n", + "u_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + ULearner'] = clip_hte(u_sep_i.effect(X))\n", + "\n", + "# RALearner (cross-fitted)\n", + "ra_sep_i = RALearner(cv=2)\n", + "ra_sep_i.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results['AIPCW sep indirect + RALearner'] = clip_hte(ra_sep_i.effect(X))\n", + "\n", + "print(f\"ATEs (separable indirect, true={true_cate_indirect.mean():.4f}):\")\n", + "for k in ['AIPCW sep indirect + XLearner', 'AIPCW sep indirect + AIPTWLearner',\n", + " 'AIPCW sep indirect + RLearner', 'AIPCW sep indirect + IPTWLearner',\n", + " 'AIPCW sep indirect + MCLearner', 'AIPCW sep indirect + MCEALearner',\n", + " 'AIPCW sep indirect + ULearner', 'AIPCW sep indirect + RALearner']:\n", + " print(f\" {k:40s}: {results[k].mean():.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "jtscz0aby", + "metadata": {}, + "source": [ + "## 5. Evaluation & Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5wqx50f06z6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total CATE estimates\n", + "Method ATE Bias RMSE\n", + "---------------------------------------------------------------\n", + "CompetingRisksTLearner 0.1207 -0.0107 0.4026\n", + "CompetingRisksSLearner 0.1286 -0.0028 0.3550\n", + "AIPCW + TLearner 0.1028 -0.0286 0.4008\n", + "AIPCW + SLearner 0.1543 0.0228 0.3686\n", + "AIPCW + CausalForest 0.1205 -0.0110 0.2935\n", + "IFLearner (total) 0.1531 0.0216 1.9732\n", + "AIPCW + XLearner 0.1227 -0.0087 0.4131\n", + "AIPCW + AIPTWLearner 0.0830 -0.0484 0.7979\n", + "AIPCW + RLearner 0.0941 -0.0373 0.7194\n", + "AIPCW + IPTWLearner 0.1037 -0.0277 0.8582\n", + "AIPCW + MCLearner 0.1407 0.0093 0.8567\n", + "AIPCW + MCEALearner 0.1218 -0.0097 0.7097\n", + "AIPCW + ULearner 0.0849 -0.0466 1.0813\n", + "AIPCW + RALearner 0.1225 -0.0090 0.4769\n", + "True ATE (total) 0.1315\n", + "\n", + "Separable direct CATE estimates\n", + "Method ATE Bias RMSE\n", + "---------------------------------------------------------------\n", + "SeparableDirectAstar1TLearner 0.0780 -0.0178 0.3982\n", + "SeparableDirectAstar1SLearner 0.0868 -0.0090 0.3544\n", + "AIPCW sep direct + TLearner 0.0250 -0.0708 1.2618\n", + "AIPCW sep direct + SLearner 0.1212 0.0254 0.8024\n", + "AIPCW sep direct + CausalForest -0.0185 -0.1143 0.6372\n", + "IFLearner (sep direct) -0.3350 -0.4308 2.3029\n", + "AIPCW sep direct + XLearner -0.0254 -0.1212 1.1692\n", + "AIPCW sep direct + AIPTWLearner -0.0140 -0.1098 2.1303\n", + "AIPCW sep direct + RLearner 0.0753 -0.0205 2.2545\n", + "AIPCW sep direct + IPTWLearner 0.1133 0.0175 2.2172\n", + "AIPCW sep direct + MCLearner 0.0653 -0.0305 2.0988\n", + "AIPCW sep direct + MCEALearner -0.0473 -0.1431 2.2027\n", + "AIPCW sep direct + ULearner 0.0938 -0.0020 3.0263\n", + "AIPCW sep direct + RALearner -0.0151 -0.1109 1.3246\n", + "True ATE (sep direct) 0.0958\n", + "\n", + "Separable indirect CATE estimates\n", + "Method ATE Bias RMSE\n", + "---------------------------------------------------------------\n", + "SeparableIndirectAstar1TLearner 0.0442 0.0085 0.0995\n", + "SeparableIndirectAstar1SLearner 0.0344 -0.0012 0.0940\n", + "AIPCW sep indirect + TLearner 0.0380 0.0023 0.3627\n", + "AIPCW sep indirect + SLearner 0.0739 0.0382 0.1958\n", + "AIPCW sep indirect + CausalForest 0.0875 0.0519 0.2209\n", + "IFLearner (sep indirect) 0.4380 0.4024 1.2984\n", + "AIPCW sep indirect + XLearner 0.0702 0.0346 0.3699\n", + "AIPCW sep indirect + AIPTWLearner 0.0529 0.0173 0.8586\n", + "AIPCW sep indirect + RLearner 0.0891 0.0534 0.8502\n", + "AIPCW sep indirect + IPTWLearner -0.0058 -0.0415 0.9890\n", + "AIPCW sep indirect + MCLearner -0.0257 -0.0613 0.8986\n", + "AIPCW sep indirect + MCEALearner 0.0462 0.0106 0.7694\n", + "AIPCW sep indirect + ULearner 0.0785 0.0428 1.1574\n", + "AIPCW sep indirect + RALearner 0.0615 0.0259 0.4486\n", + "True ATE (sep indirect) 0.0357\n", + "\n" + ] + } + ], + "source": [ + "# Collect learner-only CATE estimates for each estimand\n", + "total_results = {\n", + " 'CompetingRisksTLearner': np.asarray(cate_cr_t).ravel(),\n", + " 'CompetingRisksSLearner': np.asarray(cate_cr_s).ravel(),\n", + " 'AIPCW + TLearner': np.asarray(results['AIPCW + TLearner']).ravel(),\n", + " 'AIPCW + SLearner': np.asarray(results['AIPCW + SLearner']).ravel(),\n", + " 'AIPCW + CausalForest': np.asarray(results['AIPCW + CausalForest']).ravel(),\n", + " 'IFLearner (total)': np.asarray(results['IFLearner (total)']).ravel(),\n", + " 'AIPCW + XLearner': np.asarray(results['AIPCW + XLearner']).ravel(),\n", + " 'AIPCW + AIPTWLearner': np.asarray(results['AIPCW + AIPTWLearner']).ravel(),\n", + " 'AIPCW + RLearner': np.asarray(results['AIPCW + RLearner']).ravel(),\n", + " 'AIPCW + IPTWLearner': np.asarray(results['AIPCW + IPTWLearner']).ravel(),\n", + " 'AIPCW + MCLearner': np.asarray(results['AIPCW + MCLearner']).ravel(),\n", + " 'AIPCW + MCEALearner': np.asarray(results['AIPCW + MCEALearner']).ravel(),\n", + " 'AIPCW + ULearner': np.asarray(results['AIPCW + ULearner']).ravel(),\n", + " 'AIPCW + RALearner': np.asarray(results['AIPCW + RALearner']).ravel(),\n", + "}\n", + "\n", + "direct_results = {\n", + " 'SeparableDirectAstar1TLearner': np.asarray(direct_astar1_cr_t).ravel(),\n", + " 'SeparableDirectAstar1SLearner': np.asarray(direct_astar1_cr_s).ravel(),\n", + " 'AIPCW sep direct + TLearner': np.asarray(results['AIPCW sep direct + TLearner']).ravel(),\n", + " 'AIPCW sep direct + SLearner': np.asarray(results['AIPCW sep direct + SLearner']).ravel(),\n", + " 'AIPCW sep direct + CausalForest': np.asarray(results['AIPCW sep direct + CausalForest']).ravel(),\n", + " 'IFLearner (sep direct)': np.asarray(results['IFLearner (sep direct)']).ravel(),\n", + " 'AIPCW sep direct + XLearner': np.asarray(results['AIPCW sep direct + XLearner']).ravel(),\n", + " 'AIPCW sep direct + AIPTWLearner': np.asarray(results['AIPCW sep direct + AIPTWLearner']).ravel(),\n", + " 'AIPCW sep direct + RLearner': np.asarray(results['AIPCW sep direct + RLearner']).ravel(),\n", + " 'AIPCW sep direct + IPTWLearner': np.asarray(results['AIPCW sep direct + IPTWLearner']).ravel(),\n", + " 'AIPCW sep direct + MCLearner': np.asarray(results['AIPCW sep direct + MCLearner']).ravel(),\n", + " 'AIPCW sep direct + MCEALearner': np.asarray(results['AIPCW sep direct + MCEALearner']).ravel(),\n", + " 'AIPCW sep direct + ULearner': np.asarray(results['AIPCW sep direct + ULearner']).ravel(),\n", + " 'AIPCW sep direct + RALearner': np.asarray(results['AIPCW sep direct + RALearner']).ravel(),\n", + "}\n", + "\n", + "indirect_results = {\n", + " 'SeparableIndirectAstar1TLearner': np.asarray(indirect_astar1_cr_t).ravel(),\n", + " 'SeparableIndirectAstar1SLearner': np.asarray(indirect_astar1_cr_s).ravel(),\n", + " 'AIPCW sep indirect + TLearner': np.asarray(results['AIPCW sep indirect + TLearner']).ravel(),\n", + " 'AIPCW sep indirect + SLearner': np.asarray(results['AIPCW sep indirect + SLearner']).ravel(),\n", + " 'AIPCW sep indirect + CausalForest': np.asarray(results['AIPCW sep indirect + CausalForest']).ravel(),\n", + " 'IFLearner (sep indirect)': np.asarray(results['IFLearner (sep indirect)']).ravel(),\n", + " 'AIPCW sep indirect + XLearner': np.asarray(results['AIPCW sep indirect + XLearner']).ravel(),\n", + " 'AIPCW sep indirect + AIPTWLearner': np.asarray(results['AIPCW sep indirect + AIPTWLearner']).ravel(),\n", + " 'AIPCW sep indirect + RLearner': np.asarray(results['AIPCW sep indirect + RLearner']).ravel(),\n", + " 'AIPCW sep indirect + IPTWLearner': np.asarray(results['AIPCW sep indirect + IPTWLearner']).ravel(),\n", + " 'AIPCW sep indirect + MCLearner': np.asarray(results['AIPCW sep indirect + MCLearner']).ravel(),\n", + " 'AIPCW sep indirect + MCEALearner': np.asarray(results['AIPCW sep indirect + MCEALearner']).ravel(),\n", + " 'AIPCW sep indirect + ULearner': np.asarray(results['AIPCW sep indirect + ULearner']).ravel(),\n", + " 'AIPCW sep indirect + RALearner': np.asarray(results['AIPCW sep indirect + RALearner']).ravel(),\n", + "}\n", + "\n", + "all_truth_arrays = [true_cate_total, true_cate_direct, true_cate_indirect]\n", + "all_pred_arrays = list(total_results.values()) + list(direct_results.values()) + list(indirect_results.values())\n", + "scatter_axis_min = min(arr.min() for arr in all_truth_arrays + all_pred_arrays)\n", + "scatter_axis_max = max(arr.max() for arr in all_truth_arrays + all_pred_arrays)\n", + "scatter_lims = [scatter_axis_min, scatter_axis_max]\n", + "\n", + "all_bias_arrays = (\n", + " [np.abs(v - true_cate_total) for v in total_results.values()]\n", + " + [np.abs(v - true_cate_direct) for v in direct_results.values()]\n", + " + [np.abs(v - true_cate_indirect) for v in indirect_results.values()]\n", + ")\n", + "violin_ymax = max(arr.max() for arr in all_bias_arrays)\n", + "violin_xlim = (0.5, len(total_results) + 0.5)\n", + "estimate_ymin = min(arr.min() for arr in all_pred_arrays)\n", + "estimate_ymax = max(arr.max() for arr in all_pred_arrays)\n", + "\n", + "\n", + "def _print_metrics_table(title, cate_map, truth, truth_label):\n", + " print(title)\n", + " print(f\"{'Method':35s} {'ATE':>8s} {'Bias':>8s} {'RMSE':>8s}\")\n", + " print('-' * 63)\n", + " for name, cate_hat in cate_map.items():\n", + " bias = np.mean(cate_hat - truth)\n", + " rmse = np.sqrt(np.mean((cate_hat - truth) ** 2))\n", + " ate = np.mean(cate_hat)\n", + " print(f\"{name:35s} {ate:8.4f} {bias:8.4f} {rmse:8.4f}\")\n", + " print(f\"{truth_label:35s} {truth.mean():8.4f}\")\n", + " print()\n", + "\n", + "\n", + "_print_metrics_table('Total CATE estimates', total_results, true_cate_total, 'True ATE (total)')\n", + "_print_metrics_table('Separable direct CATE estimates', direct_results, true_cate_direct, 'True ATE (sep direct)')\n", + "_print_metrics_table('Separable indirect CATE estimates', indirect_results, true_cate_indirect, 'True ATE (sep indirect)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "m8dvl6uozf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7L3uPdtddd7lzZ5Pe1ms9v9Teqhxt8G1JcTs3VqFoA28b1PeuVrPYbDHXkgX2XAzmubPCCStcsHP4uc99zi29f/nll10fXuu5axPovR/DPffc486/VRJaIQIAjCeWNLe/qzZ2saIq+/tsf7ctmb0xbMLW5MdUf/rTn9zHk08+eUA/b2M3iyEWK/J/ky2W2RjR4orFG2vtYuNDKzSz+GIJ9kJdATzQMad9bm1f7Tn67Gc/6wrorMjRxrs2Js63zsmzeGyroy0W2nsCS4LnWQy1KnzLs1jBnd2vTVxfddVVgx6fG3vvYnHY4rzldTaURwE2mgeMck1NTVbW7X3kIx/Z4G1ffPFFd9vPfOYzPa4/99xz3fX//Oc/u66bOXOmu+7xxx/vuu7BBx9018ViMe/dd9/tuv62225z1z/yyCNd15166qnuui996Utd1+VyOe/II4/0wuGwV1tb6677v//7P3e7u+++u8cxPfDAA+tcv/3223sHHHDAOo/Lfm/v32+3s+t+9rOfdV2XTCa9SZMmeR/72Me6rvvBD37gbveHP/yh67qOjg5v7ty5fT6moqIid+x2efvtt71rrrnG8/l83rx589zj685+/uKLL+76uqyszDvzzDO99bHjtsdp7rvvPi8UCnmf/exnvWw223WbH/7wh+6+8+dwQ+699951Hkt39lzb89KfxYsXe4FAwPve977X4/qXX37ZCwaDPa5vb29f5+evuOIKd466v2byr4/zzz9/ndvb9fYasfObN3/+fHf9jTfe2HXdpz/9aW/y5MleXV1dj5//1Kc+5c51/ljyr48tttiiz+MDgM2N2E3s7suqVau8CRMmuJhl70O+8IUveL/85S+9xsbGdW6bf0/SH3tPsvXWW3uHH354j/cnFgdnz57tHXrooT2u6+2JJ55Y533UXXfd5a7bd999vUwm0+P2A33f9fOf/9zz+/3u/V93t956q/v5//znP13X2dd221dffbXfxwkAY9mzzz7r/hY+9NBD7mv7ez5t2jTv7LPPXue2drvuY81Fixa56y699FI3brQY869//cvbZZdd3PU21jTHHnus+7qhoWFAx/SXv/zF3d7+npuVK1e6rx999FGvpaXFjRvtNuaVV15x3+s9jhyI/PH3N4YdqP5yCN3j2jPPPNPvzw90zGlx0eJed3ZOJ06c6J1xxhnrPK7S0lJvzZo1PW5vuQP7Xvfb55+jqqqqjRqf5+OzxVlguBXmVBnGlXxLCqvW3ZC//vWv7uNXv/rVHtdbRbrp3TfdKq5s1jgvvxu4VTBZu47e1/duZ2K67xCen721pU7/+Mc/3HVWnWxLlKytiW1okr9Y1ZRVnQ+mqqw3+/nuvc9s9t5mx7sfp1XH23Jjq4rKs+XSNnvcF6u4stlnu9gyZKtqtmVrtsFofpfv/thssFX+r1ixYoPHbj3WrYLNqvStV133mfv8rLL9TqvoGm5WEW6/x2a5uz9HVvm39dZb93iObClZ93Nlt7MVEva+rnfLH2MV63055JBD3NK2PKvcs6Xs+efO7u++++5zKyPs8+7HZbP9tulr7zY5VtXX/fgAYKQQu/s3nmO3VYbNnz/fVb9bVbet/LMVgVZ9ZquperfDW58XX3xRb731lvt5q0DLx0g7FwcffLD+/e9/dx1H99iYTqfd7e082TH31XLOzrNV12/Mc2fv+6z63DZN7R678+0Fer/vs/Yz9n4UAMZrFbrFBmuTZSxmWZyxNpW2cmkgrDLc4p+N3WyFkVWiW0WzVbcP9j2JsbGdxbd8r/P8ynGrjLc4YOO2/Cqr/MeB9EO39rTd40K+tauN67pfb19vLoMZc1pczK90tvhqm4XbymtrjdtXLLVV1va89MXeB3RnbWEsNuefq8GMz41Vutuqf2C40c4Fo54lFo0tF9oQ63lpAa/3bt72x9YGSvb97ronyo0lu431ruzr+u49zI39Lmsz092cOXN6LKO2AZ4FHxsg9mXNmjXaWLYkuffg2JZtv/TSS11f22O2ZG3v2/W347kN0m0JvrEWJdamxo5xIMlZu60lcu382SSB9QI/5ZRT1jlH1s7GBqHWj9SWZPVmb5xsSZct2zr//PPdYNjeBNnS8eFYJmfPkb1psIDcF3vTlGftVmx5ty0L7P166P2GxzZpzfdS7a33ay//3OXv05ar2TJB2yneLgN57XTfJR4ARhKxu3/jPXbb3ja2VP/mm2928df2aLFkh8VW+579/EDYz5r1tYWxuGzn1vYtsSXjtrTc+r52T9b3lazoL54O5Lmz47JWOP0lDojdANDJkuSWLLcEer5/eb6A7Qc/+IEefvhhHXbYYRs8XdYuxGKTxRob8+f3mOjrPUn3FiD9yd9H90T5Lrvs0hVTLcne/Xv5CdUNsWI7azvb2zHHHLPO5OqG9i0bKoMdc9rx23PzxhtvuEnp9cXN9Y1Ne4+F863nbCxsz9dgxufGCg9oZYrNgSQ6Rj37I2obdr7yyisD/pkNVV3l9VVltL7rB1MhlWczqJZA72+DlP4GWQMxlMfZ/T6tSjrPZqCtmsqqzvL95PpjM8U2i2y9522z0O9///tuYGwzyfleocYGyXaxlQPPPvusm73uzt6gWAWZzTDb6gGryPvNb37jqrjsfvt73BvLniN7zVjvt/4qz/Jv9GxFgc26Ww9UOy+2oYoNyG1jlN6Vd/bmrb+k/4aeu/x9WcKivwSBVUF0RxU6gNGC2N0/Yncni7tWeGAX20zOBsr2XmmgSfR8nLT3Gr37sPaO37bnjCXQra+rrUC04gj7/dY7ta+q+f7i6UCeO7s/65F+7bXX9nnb3oUaxG4A45XtCWF7f1gi3S69WUwYSBLd4kf38WtvNmYztj+FjVUHwirLbbWUJZgtUd59bzb73PYDsSSyVavbBPRANsa2Daa7r2ayXuT5fdl22mmnrus3tJfJUBrMmNM2+bQxryX9zzvvPJfjsLhok9T5PvQDjW8DGQsPZHw+kN8FDCWS6CgItlGVzYw+8cQTPdqv9GYbVtkfXJu5tKW03QOUBUD7/lCy32VLePPV58Y2jTL5TUisksxau9iy6g39cR9o8n8w7DHbplkWkLrfv232NRCW7D7nnHN06aWX6sknn3Sbjmzo9rYhl11s1to2JbNNQbon0e1Nhm1KZklx26Dr0UcfdbP93Vny2arY7GID0csvv9xtuGqJ9fW9SdoY9hzZ+bHZ8u7PZW/2xsueX5uBtyq9vIceekhDzSZXbMmhJe6H+vECwOZA7N544y12W9W7JQ0smTJQ+ZZoNmGzoThpG4BacsCq5/JsszN7bzjU7LisZY2dg+F4XwcAY4UlyS0Re9NNN63zPSvCssIsS2RvaoLUWpVYoteSwINJotuqKRvHW8tOSxp3T6LbCicr9rJcgLUtGQhr3dW9fVd+5bol4a0NzUgYzJjTYqnFa3tuusc3a6cz1AY6Pgc2N3qioyDYrK1V/Fp1kiXEe7OZz+uvv94tQTbXXXddj+/nq4Gs0mmo/ehHP+r63P7Q29e2vMgGT/nqbAtK1uuzN+sh1n0AZ49xqAd0VkluldLdq8ht4HjHHXcM+D6sgisej+vKK6/s9zb2GHsvibY3RbaKIJlMrnN7qwKzJdx2G6vu7j57bZXeveWrzPq6r01ly81thtuSDb2r+O1r689m8rPg3W9jn9trb6jZ77I3ZNajrq9VGLb0DgBGM2L3xhursdt6r1vP8t6efvppF2u32WabAT8+SzrYINsq+KzP7PripMXU3vHdWtIMtN/uYNj7Pnvu+nquLOnS1+MHgPHG/h5aMtYm3K3tV++LtT6x9isbWgk9EFaEZ5O/1nLsD3/4wzrft/3MbC+R7vI9zi2PYBXn3SvRrVjOJp+tHVr32xaiwYw5+xoLW1y3QseRGp8DmxuV6CgINkj65S9/6fptWoW5VQHPmzfPBbzHH3/cbeJkS4vOPvtsV2lkVeuWjLZ+YjYws8phW3aU37BkqFhVlrUasd9pvdtsuZHNSH/zm9/satNix2CtUGz22zbBsiVplmS3ank7bkvA2huF/IDQZrwvu+wy1/fUBqn5jag2lv1uS+wff/zx7vxYwLdZ//ySs4FUSVVVVbmNOqx/qfX57F7ln2dvcqxXqD0WW45mS6xs5v6ZZ57pUfnVXXV1tavitjceNvNty+Gsn9l3vvMd187FJj2sGs+q4ux32/1v7JsUq96z89qb9bez32Pfu+CCC1xFgL1WbEbeevNZBYT12bM3VrYU0F6L9rkNkK36zd5w9O6NPlQs8WHVe/bask3OrHLBkhS2cYud274SFgAwWhC7N95Yjd0///nP3eM49thj3Xse619qx2bL4u2x2fun7ixx0VfsrqysdFXzlhCxanmriLfHasdh8dlip8XofJ94S9LY77ZJAIulNuC3x2nnaKidfPLJuueee9ymaXYcthLRkvXWP9aut0mI3m3sAGC8seS4xaDuG2h3ZyuobDxtMcNyAJvqZz/7mRuHW3LWKtOt4M0K2GxMbq1kbCWUTcp279lt7bcsXljS3CaXu7Okuo0DLR7b3/nRzuKs5S16s/cYAx1zWiy1iQ+L4RbrbaxsKwXs9n1NZm/qe8iBjM+Bzc4DCsiCBQu8z372s96sWbO8cDjslZSUePvss4934403eolEwt0mnU57l156qTd79mwvFAp506dP9y644IKu7+fNnDnTO/LII9f5HfbP4swzz+xx3aJFi9z13//+97uuO/XUU72ioiJv4cKF3mGHHebF43Fv4sSJ3sUXX+xls9l17vf222/3dtttNy8Wi7nj3mGHHbyvf/3r3ooVK7pus2rVKndM9n37fQcccIC7/pFHHnFf28c8+97222+/zu+x47LH1t0777zj7td+94QJE7yvfe1r3n333efu88knn1znMfXFHmcgEHC36X6u7PGaZDLpnXfeed5OO+3kjt/uxz6/+eabe9xPX8f99ttve5MnT/a23XZbr7a21nv44Ye9j3zkI96UKVPc82wfjz/+ePf89+Xee+9d5/x0Z+fDvt/X5dOf/nTX7eyc7Lvvvu7Y7TJ37lz3WnjzzTe7bvPaa695hxxyiFdcXOxVV1e71+P8+fPdfd11110DOpd9vcbyx9n9/JrVq1e729rr2F7PkyZN8g4++GD3esrLvz7sPADAaEPsJnbnvfTSS+69wq677upVVlZ6wWDQxf/jjjvOe/7553u8biwe9he7t9xyy67bvfDCC95HP/pRr6qqyotEIi6WfuITn3DvJfIaGhq8008/3cVti9+HH36498Ybb6wTdy2O2/0/88wz67yOB/O+K5VKeVdddZW7vR1TRUWFew9o70+bmpo2+H4AAMa6o48+2otGo15bW1u/tznttNPc+Keurq7Pv5l9jdHXp7293bvmmmu83Xff3cUCG2duvfXW3pe+9CU3Hu3Nxp92/yeccMI637v22mvd92z8urHyx9/fGHagLNbk8wa95eNaf5elS5cOeMyZy+W8yy+/3MU8i2277LKLd//9968TB9f3vFjuwL5nY/6+jtN+truBjM/7i8/AcPDZ/zZ/6h4ofFb5bn3BhnrWdXOxljfWL3XZsmWucgsAgLGO2A0AAABgY9ATHRgnPee6s76qt912m9vJnAQ6AACjD7EbAAAAGD3oiQ6MA9b7zfq62QZftoGY7UxuvTmtxxwAABh9iN0AAADA6EESHRgHDj/8cLf5liXNbXMr2/zDNlAZik1aAADA0CN2AwAAAKNHwfREt8TfJZdc4ipoV61a5XZHtr6WF110kdsRGQAAjC7EbgAACguxGwCAAq9Ev+qqq3TLLbfopz/9qbbffns9++yzOv3001VWVqYvf/nLI314AACgF2I3AACFhdgNAECBV6IfddRRmjhxon784x93Xfexj31MsVjMVacDAIDRhdgNAEBhIXYDAFDgleh77723br/9di1YsEBz5szR/Pnz9dhjj+naa6/t92eSyaS75OVyOdXX16uqqooWMACAUcfmtVtaWlzLMr/fr0JH7AYAjHXEbsbdAIBxEru9ApHNZr1vfOMbns/n84LBoPt4+eWXr/dnLr74Yquy58I54DXAa4DXAK+BgnoNLF261BsLiN0j/1riwjngNcBrgNcAsZvYzd8B/g7wGuA1wGuA18Cmj7sLpp3Lr3/9a5133nn6/ve/73qiv/jii/rKV77iKtFPPfXUAVWiNzU1acaMGVq6dKlKS0s349EDANCPv/5V+p//kaJRNV9/vaZ/4hNqbGx0e34UOmI3AGBMInb3wLgbADAeYnfBJNGnT5+u888/X2eeeWbXdZdddpnrh/7GG28M6D6am5vdybFkOkl0AMCIsxB89NFSICDdeaeaQ6ExFaeI3QCAMYfYvUGMuwEAYzF2F0xP9Pb29nX61AQCAdfnHACAgvL661JtrbT//tJvfiPF45LPZ6NOjSXEbgDAmEHsHulnAACAEY3dBZNEP/roo/W9733PtWOxdi4vvPCCa+VyxhlnjPShAQAw8BnwH/9Y+vKXpT33lP75T6moaMyePWI3AKDgEbsZdwMACsswxe6Caediu6Z+61vf0u9//3utWbPG7aB6/PHH69vf/rbC4fCA7oNlZQCAEdPYKH3uc9K990qf/az0wx+uE8jHWpwidgMAChqxm3E3AKCwNA7fuLtgkuhDYawlJwAABeSww6Snn5buuEM67rg+b0Kc4pwAAEYRYvdG4f0MAGAsxu6CaecCAEDByWalhgapulr6wQ+kkhJp1qyRPioAANAfYjcAAIUlu3nG3STRAQAYDsuXSyefLHV0SI8/Lu2wA+cZAIDRjNgNAEBhWb75xt0k0QEAGGp//rN0+ulSJCL94hedO4ADAIDRi9gNAEBh+fPmHXf7h/XeAQAYby64QPrwh6V99pHmz5cOPHCkjwgAAKwPsRsAgMJyweYfd5NEBwBgKM2bJ914o/SHP3T2ZAMAAKMbsRsAgMIyArGbdi4AAGwKz5PuvFN68cXOIH7iiZxPAABGM2I3AACFxRv5cTeV6AAAbKzGRun446XPfEZKJDp3BQcAAKMXsRsAgMLSODrG3VSiAwCwMZ54QjrhBKmhQfrNb6RPfILzCADAaEbsBgCgsDwxesbdJNEBANgY994rTZ4sPfKINGsW5xAAgNGO2A0AQGG5d/SMu0miAwAwUMuXS888Ix1zjHTllZLfLwUJpQAAjFrEbgAACsvy0Tnupic6AAADcf/90k47SV/7mpRKSeHwqAjkAACgH8RuAAAKy/2jd9xNEh0AgPWxjUvOPls6+mhp772lp57qDOQAAGB0InYDAFBYEqN/3E0SHQCA9fnqV6Vbb5VuuEH64x+l6mrOFwAAoxmxGwCAwvLV0T/uJokOAEBvnietWtX5+YUXSk8/LX3pS5LPx7kCAGA0InYDAFBYvMIad5NEBwCgu8ZG6fjjpd13l9rapKlTO3uyAQCA0YnYDQBAYWksvHH36OjMDgDAaPDEE9IJJ0j19dLtt0tFRSN9RAAAYH2I3QAAFJYnCnPcTSU6AADG+q/tt580aZL04ovSJz/JeQEAYDQjdgMAUFhuLdxxN0l0AADMvHnS178u/fvf0uzZnBMAAEY7YjcAAIVlXuGOu0miAwDGr7/8Rfr4x6VsVtp3X+nyy6VQaKSPCgAA9IfYDQBAYfnL2Bh3k0QHAIw/yaT0la9IRx3V+bltZAIAAEYvYjcAAIUlObbG3WwsCgAYX958U/rUp6TXXpNuuEE66yzJ5xvpowIAAP0hdgMAUFjeHHvjbpLoAIDx5ZFHpI4O6amnpJ13HumjAQAAG0LsBgCgsDwy9sbdtHMBAIx9TU3Sz37W+fnnPy+98MKYCeQAAIxJxG4AAApL09ged5NEBwCMbU8+2Rm4v/xlafXqziVksdhIHxUAAOgPsRsAgMLy5Ngfd5NEBwCMTbmcdMUVnbt/T5rUOQs+ceJIHxUAAOgPsRsAgMKSGz/jbpLoAICx6frrpQsvlL7xDenf/5Zmzx7pIwIAAOtD7AYAoLBcP37G3WwsCgAYW1aulCZP7uzBtttu0v77j/QRAQCA9SF2AwBQWFaOv3E3legAgLEhmZTOOUeaM0d6910pHh8XgRwAgIJF7AYAoLAkx++4u6CS6MuXL9dJJ52kqqoqxWIx7bDDDnr22WdH+rAAACPtzTel979fuvlm6bLLpBkzRvqI8B5iNwCgT8TuUYvYDQDo05vje9xdMO1cGhoatM8+++jAAw/U3/72N02YMEFvvfWWKioqRvrQAAAj6f77pU99Spo6tXNH8F124fkYJYjdAIA+EbtHLWI3AKBP9zPuLpgk+lVXXaXp06frrrvu6rpu9hhuVg8AGKDtt5dOPdUChVRczGkbRYjdAIA+EbtHLWI3AKBP2zPuLph2Ln/605/0vve9T8cdd5xqamq0yy676I477ljvzySTSTU3N/e4AADGgKeekg47TLK/6zahetNNJNBHIWI3AKALsbsgELsBAF2I3YWZRH/nnXd0yy23aOutt9aDDz6oL37xi/ryl7+sn/70p/3+zBVXXKGysrKui1WyAwAKWC7XWXG+775SS0vnBaMWsRsAQOwuLMRuAACxu28+z/M8FYBwOOwq0R9//PGu6yyJ/swzz+iJJ57otxLdLnlWiW6J9KamJpWWlm6W4wYADJGVK6VTTpEefli64ALpkkukUGhMnV6LUzbpO1biFLEbAMY5YnfBIXYDwDhH7C78nuiTJ0/Wdttt1+O6bbfdVvfdd1+/PxOJRNwFADAGvPKK9Prr0kMPSQcfPNJHgwEgdgPAOEfsLjjEbgAY54jdhd/OZZ999tGbb77Z47oFCxZo5syZI3ZMAIBhZquJbrutcznZoYdKb71FAr2AELsBYBwidhc0YjcAjEPE7rGVRD/nnHP05JNP6vLLL9fbb7+tX/7yl7r99tt15plnjvShAQCGg02cvv/91rtLevHFzutiMc51ASF2A8A4Q+wueMRuABhniN1jL4m+++676/e//71+9atfad68efrud7+r6667TieeeOJIHxoAYCjZVh133SXtuqvU3i49+WTn5yg4xG4AGCeI3WMGsRsAxgli99jdWHQojLUN2wBgTPrzn6UPf1g6/XTphhuk4mKNF8QpzgkAFCRiN2PMbng/AwAFgNitMbuxKABgjFuxQpoyRTryyM7NQw85ZKSPCAAArA+xGwCAwkLsHvvtXAAAY5RtGnrVVdLs2dLjj0t+Pwl0AABGM2I3AACFhdi9yahEBwCMnJUrpVNOkf7xD+kb37BGnDwbAACMZsRuAAAKC7F7SJBEBwCMjOeekz74QSkQkP7+d+nQQ3kmAAAYzYjdAAAUFmL3kKGdCwBgZGy1lXTMMdL8+STQAQAoBMRuAAAKC7F7yJBEBwBsPgsWSAcfLC1eLJWVSbffLtXU8AwAADBaEbsBACgsxO5hQRIdADD8PE/62c+kXXeVli2TWls56wAAjGbEbgAACguxe1iRRAcADK/mZunkk6VTT5WOO66zJ9u8eZx1AABGK2I3AACFhdg97NhYFAAwvFaskB5+WLr7bumEEzjbAACMdsRuAAAKC7F72JFEBwAMvVxOuuOOzgr0uXOlRYukaJQzDQDAaEXsBgCgsBC7NyvauQAAhtbKldLhh0tf+IL04IOd15FABwBg9CJ2AwBQWIjdmx2V6ACAofO3v3X2Pg8EpL//XTr0UM4uAACjGbEbAIDCQuweEVSiAwCGxquvSh/6kPS+90nz55NABwBgtCN2AwBQWIjdI4ZKdADAplm+XJoyRdp++84NRD/wAcnPHC0AAKMWsRsAgMJC7B5xZDkAABvH86Sf/UzaZhvpF7/ovO6gg0igAwAwWhG7AQAoLMTuUYMkOgBg8JqbpZNP7ux//vGPS8cey1kEAGA0I3YDAFBYiN2jCu1cAACDs2RJZ8X5mjWdFegnnsgZBABgNCN2AwBQWIjdow6V6ACAwbH+50ccIb3wAgl0AAAKAbEbAIDCQuwedUiiAwA2bNUq6cgjpccfl4JB6Uc/krbckjMHAMBoRewGAKCwELtHNdq5AADW74EHOnuf+3xSKsXZAgBgtCN2AwBQWIjdox6V6ACAvlnC/NxzpQ9+UNp1V+mll6QPfICzBQDAaEXsBgCgsBC7CwZJdABA31papN/9TvrBD6S//EWqqeFMAQAwmhG7AQAoLMTugkE7FwBAT3ffLR14YOdGJq+/LkUinCEAAEYzYjcAAIWF2F1wqEQHAHRqbpZOPlk66STpV7/qvI4EOgAAoxexGwCAwkLsLlhUogMApGeekY4/Xlq9Wvr5zzsT6QAAYPQidgMAUFiI3QWNJDoAjHcNDdJBB0lz53buCL7VViN9RAAAYH2I3QAAFBZid8EjiQ4A45VVnZeXSxUV0t/+Ju2xhxQOj/RRAQCA/hC7AQAoLMTuMaNge6JfeeWV8vl8+spXvjLShwIAhefBB6Udd5Quu6zz6333JYGOYUfsBoBNQOzGCCB2A8AmIHaPKQWZRH/mmWd02223aUdLAAEABi6Vks47TzriCGmXXaSzzuLsYbMgdgPARiJ2Y4QQuwFgIxG7x6SCS6K3trbqxBNP1B133KEKa0EAABjoH1Bpn32k66+XrrlG+utfpYkTOXsYdsRuANjoP6DEbowIYjcAbPQfUGL3GFVwSfQzzzxTRx55pA455JCRPhQAKCzFxZ0V6I8/Ln3ta5K/4EIAChSxGwA2ErEbI4TYDQAbidg9ZhXUxqK//vWv9fzzz7tlZQORTCbdJa+5uXkYjw4ARqGWFhsFSYcdJp10kvTd7470EWGcIXYDwCARuzHCiN0AMEjE7nGhYMoQly5dqrPPPlt33323otHogH7miiuuUFlZWddl+vTpw36cADBqPPustOuu0u9/L4VCI300GIeI3QAwSMRujDBiNwAMErF73PB5nuepAPzhD3/Qscceq0Ag0HVdNpuVz+eT3+93Fefdv9dfJbol0puamlRaWrpZjx8ANptcTvrhD6ULLpB22kn61a+krbbiCSgAFqds0nesxCliNwAMELG7YBG7GXcDGKeI3eMudhdMO5eDDz5YL7/8co/rTj/9dM2dO1ff+MY31kmgm0gk4i4AMK5kMtJvfiN95SvSZZdJ4fBIHxHGKWI3AAwQsRujBLEbAAaI2D3uFEwSvaSkRPPmzetxXVFRkaqqqta5HgDGpQcflGpqpF12kf7v/2wmcaSPCOMcsRsANoDYjVGG2A0AG0DsHrcKpic6AKAfqZR03nnSEUdId9zReR0JdAAARi9iNwAAhYXYPe4VTCV6X/71r3+N9CEAwMh6+23p+OOl+fOla66RzjmHZwSjGrEbwLhH7EaBIXYDGPeI3Sikdi4AgD56sB1+uOT3S48/Lr3vfZwiAABGM2I3AACFhdiN95BEB4BC09IipdNSZaV0zz3SnDnWwHKkjwoAAPSH2A0AQGEhdqMXeqIDQCF57jlp112lM8/s/Hq33UigAwAwmhG7AQAoLMRu9IEkOgAUglxO+sEPpL32ksrKpO98Z6SPCAAArA+xGwCAwkLsxnrQzgUACiGQf+Qj0v33S1/7mnT55VI4PNJHBQAA+kPsBgCgsBC7sQEk0QFgtLONQ20DUWvhcsQRI300AABgQ4jdAAAUFmI3NoAkOgCMRqmU9K1vSbGYdMkl0llnjfQRAQCA9SF2AwBQWIjdGAR6ogPAaLNwobTvvtK110qlpSN9NAAAYEOI3QAAFBZiNwaJSnQAGE3uvlv64helmhrp8cel3Xcf6SMCAADrQ+wGAKCwELuxEahEB4DRwvOkP/6xcxPRF14ggQ4AwGhH7AYAoLAQu7GRqEQHgJH23HNSbW3npqG/+IUUDo/0EQEAgPUhdgMAUFiI3dhEVKIDwEjJ5Tr7nu+1V+dHmxEngQ4AwOhF7AYAoLAQuzFESKIDwEhYvVo68kjpa1+Tvvxl6f77JZ+P5wIAgNGK2A0AQGEhdmMI0c4FAEbCSSdJL70k/e1vnW1cAADA6EbsBgCgsBC7MYRIogPA5pJOS2vWSFOnSjfdJJWWSpMmcf4BABitiN0AABQWYjeGCUl0ANgcFi6UTjhBymalZ56R5szhvAMAMJoRuwEAKCzEbgwjeqIDwHD75S+lXXaR6uqkm2+m9zkAAKMdsRsAgMJC7EahJNE9z9Maa1MAAPivs8+WTjxROvpo6YUXpD324Oxg1CB2A0AfiN0YxYjdANAHYjdGUxI9Ho+rtra26+sjjzxSK1eu7PraEuiTJ08e+iMEgELkeZ0fDzhA+slPpF/8orMHOrAZEbsBYBCI3RgFiN0AMAjEbozGnuiJRMLNeuf9+9//VkdHR4/bdP8+AIxL9nfwuuukF1/sTJ5/9KMjfUQYx4jdADAAxG6MIsRuABgAYjcKvSe6z+cbyrsDgMJiLa2OOkr66lelCROkXG6kjwjYIGI3gHGN2I0CROwGMK4RuzHaK9EBAOvxj39IJ58sZbPSX/8qffCDnC4AAEYzYjcAAIWF2I1CSKLbbHf3Ge/eXwPAuPbII9IOO0g/+5k0adJIHw3gELsBYD2I3RiFiN0AsB7EbowgnzfARuZ+v19lZWVdifPGxkaVlpa6643dTXNzs7JWhTlK2fHZY2hqanLHDgCb5J13pCeflE44Qcpk7A9l5wUYJXGK2A0AvRC7McSI3cN/TgCMc8RujJI4NeBK9Lvuumtjjw0Axp5f/Ur6/OelqVOlj39cCodH+oiAdRC7AaAbYjcKALEbALohdmMUGXASffbs2dp7770VDNJGHcA41toqfelL0k9+0lmBfsstJNAxahG7AYDYjcJC7AYAYjdGpwH3HTjwwANVX18/vEcDAKPdeedJ997bmUT/xS8klqhiFCN2AwCxG4WF2A0AxG6MTgNOog+wdToAjD3292/x4s7PL71Uev556dRTbeenkT4yYL2I3QDGLWI3ChSxG8C4RezGKDeoHfDym4qOhCuuuEK77767SkpKVFNTo2OOOUZvvvnmiB0PgHFizRrpqKOkvfaS2tqkmhppzpyRPipgwIjdAMYdYjcKHLEbwLhD7EYBGFSD89NOO02RSGS9t/nd736n4fDoo4/qzDPPdIn0TCajb37zmzrssMP02muvqaioaFh+J4Bx7uGHpZNOkrLZzvYt/K1BASJ2AxhXiN0YA4jdAMYVYjfGYhLdqsBjsZhGwgMPPNDj65/85CeuIv25557T/vvvPyLHBGAMu/566ZxzpIMOkn7+c2ny5JE+ImCjELsBjBvEbowRxG4A4waxG2M1iX7DDTe4xPVo0NTU5D5WVlaO9KEAGGt92Kx11b77SpdfLn3965J/UJ2vgFGF2A1gzCN2Y4whdgMY84jdGMtJ9JHsy9ZbLpfTV77yFe2zzz6aN29ev7dLJpPuktfc3LyZjhBAQfr1r6U775T+8hdpt906L0ABI3YDGPOI3RhjiN0AxjxiNwqUf6h2CX/99dd17rnnanOw3uivvPKKfm3/8DawGWlZWVnXZfr06Zvl+AAUGNsw9IwzpOOPl6qrbQZupI8IGBLEbgBjFrEbYxSxG8CYRezGeEmiP/LII+u0Tmlra9OPf/xj7b333tp+++3X6Vs+HM466yzdf//97nimTZu23ttecMEFru1L/rJ06dJhPz4ABeaFFzorzu+5R7rrLunuu6Xi4pE+KmBIELsBjEnEboxhxG4AYxKxG+OpncsBBxzQ9fl//vMflzy/55571NHRoXPOOUd33nmn5s6dO6wz8l/60pf0+9//Xv/61780e/bsDf5MJBJxFwDo10svSfG49Nxz0jbbcKIwphC7AYxJxG6MYcRuAGMSsRvjqRJ9zZo1uvrqq12i/OMf/7jKy8tdMtvv9+uMM84Y1gR6voXLL37xC/3yl790u5WvWrXKXSyJD2B8sMm0hraUVjS2u48bWu7ar9pa6eabOz8/5RTpqadIoGNMInYDGDOI3RgniN0AxgxiN8YYnzfALFQsFnPJ85NOOkmHHnqoS56bUCik+fPna7vtthveA+1nY9O77rpLp5122oDuwzYWtd7o1tqltLR0iI8QwHCzxPnbta3K5Tz5/T5tNaFYFUXhwd3JP/8pnXSSlE5Lr7wiTZw4XIcLDNpQxyliN4ChZkOHxva0OtIZxUJBlcdDfb5PH+jtBoTYjVGM2D385wRAASJ2YxTb2Dg14HYuM2fO1GOPPaYZM2a4z4e78ry3ja44BTBm2EDcEugTSiKqbUm6rys0wCS6Jc0vvli68krpoIOkn/+cBDrGPGI3gKFmifGBTGgP9HbrRezGOETsBlDQiN0YwwbczuWNN95w7VRWrlyp3XffXbvttpt++MMfuu9tdFUJgLHdNmWIWSWbDcQtgW4f7esBu/FG6eqrpcsvl/7+d2ny5OE8VGBUIHYDGOr3A90ntO2jfZ2/3fKGNi2qbXUfVzV1KJvNqbo4rOb2lBbXtQz+PQWxG+MQsRvA5hq39/75XC636XkAYjfGsAG3c+mutbVVv/rVr1wrlSeffNJtfnLCCSfomGOO0YQJEzRasawM2IxtU4ZQfkl4eyqtVMZTOOhTPBxSWSyopo7M+peKL1wobbmllEx2tm/ZbbeRehjAiMYpYjeA/vTXeqWv9wOmv+ua2lNa2ZzQlNKofH6fLCLb7VY0JzS5NKqyeHhg7ymI3SggxO7Ne04ADN24vb41qReWNqjuvSI1+/l0zpNlCQd9f8RuFJCNjVMDrkTvrri4WJ/97Gf1+OOP69VXX3VV6RdddJGmTJmyMXcHYJTpXWVmyevBzEh3n9G2wGyXTZnNzi8JX9aQUF1byiXQLZhbAt2uX1rf4T7a7bq0tUlnnCFtv7307rtSJEICHeMasRtAfyx+vrWmRa+taNaT79RpcV1bj6pzqyZveq+a3K7fsrpI0ytjbnBtCff87YojQWUynoqiQcVDAU0qi6qyKOQS6LOri7oq1/tF7AaI3QAGzMbpFp8tNttH+3owleermju0YFWzG0s/885a/XvBGi1vaO+cXG9LamVT+4bH8cRujCOD6IXQt2233VbXXHONrrzySv3pT38amqMCMHrapvjsY0qrmpoUCvhVGgtrq5q+Z6TzlWwrGtr00vJmpTMZhYJB1ZSEVRwNr3c2u3sVXCTgV1NHWk0dKZXHIwoFfH32Qu+e7F/TnHBLx+264tdfVcnpJ8u3dKl0663SjBmb6cwBhYHYDaA7i502+K5rTWlNS0JtyYzKYqGu9wOL6tpchblVlme8NhfLp5THu34+f7um9rSCQZ9aO9IKBPwqU8jF8VTWc/eRzuZUUxLpGoj3qH5f8Kp8xx8vEbsBYjeAAbFV2hafbQLb4u+W760O62uVma3itkny596tV2sqq+JwQOWxkBt317cl5cmnVc0JJTM5rWhMqC2Vce8LbBLcYnqf4/gXX5Q+9SliN8aNAVeir1ixQueee64ree/Nyt8vuOAC7bPPPkN9fABGgFWVWZC0KjOrPrPZ55VNSRdsmztS/VaR5SvGn363QY+9Xasl9Qm9uKRBSxo6evRO7WsWPL8UzarKH1+4Vg+9vkYvLm3WYwvrXPC2ZP47a1q0tL5NS+s7K9wjQb/aUxm9vrLJvSF4ZXmTan/2axV/YF9lI1Hp+eel006zjRs28xkERgdiN4CBiAYDWtHYoeeX1Ks1mdGalqSbmLZYbYPscMCnySURzeqnmjz/vmHbySXad8tqTSmPydLkllS3GB4JBpTJ5hQO+N3XFvMtblvVu1W/1/3819L73y/FYsRujHvEbgAD7V/uCtD8Pk2rjLo4bW1P87d7Y2Wza9WyZG27G2e/u7Zdzy9p0AtLG/XKsiY9v6RRC1a1uEn02uakq7AtiwZVFAmqOBpw91/b0qFoKND3SrLf/17ac09iN8aVASfRr732WpdA76tXjPWRaWlpcbcBUPisD6rNMluVWSTkVzgYUFVxWPWtKVdFZjPZ69tsLB4MKOfJJbkDAZ8ymdw6m4G6hPuaVr2+ollPvVOnt1a3uA3ILNne2JFUfVvCzZY3t6eVzWZcMr+xI+0q5J5/t14Pv7HavSHIeZ5aExktqW9TbUtCq7fZQe+e8jmt+fsj0jbbjPSpBEYUsRvAwDcg85RKZ1XXmlRtc0LLG9tdi5dlDe16p67NJdlt01Cb1Lake++NRBvdcvLOe0qkMy6m20qyd2pb9E5ts4J+X2cS3vO0uiWhN1c1a2Vjh3t/8Mb0bbT2c2fKe+IJYjfGPWI3gL7idb5gLd/K1CajF67pXCm2cE2r24vE2p7mb7dobZuW1bcr8l4SvLE9qaxn/c49N2GeTGf1bn2bVjcl1JSwmN7uJsDTqawW1bYp5A8onfXc+4Hu4/iuYL/77tJXvyo9+SSxG+PGgNu5PPDAA7rV2iL045RTTnF90q+66qqhOjYAo4AFy9JYyH0eKotom0mlruIsH5wzGRtwp1QaC8jn86s1kVY6l3NJ94znqaY4oplVcRdsi8IBF7TzCXerarfqdkvONyfSblD+7to21TWn3OYma5pqlfFymlUVVzQUVCKddYNway9jg3wbsM+qKtIOC57XAZdfqkd+cKeWl1Qr++UL9P7ion43SgPGC2I3MH71FQPzsbv3BmSJTFZlsbDKiyJaYv3Qczm9tbpVsVBAWU9uEB4PBxQLB9ztH31ztRbWtao0GlYs5FdJNKyWVEYl4aD7WBoOalVrQi8va1JbKiufz3OT60sbOtx7gpJIQDXPPK73Xfs9/fibP1KsplpFZ35Du2T9qhjpEweMMGI3gL7idfdWpjYBvbShzbVhqS6OaG1bSkWhzrG27V+yvL7DTVpbEj6ZsvG5FPBJDda6pcX2GcuoMeAp7Pe7xHlxJKxEKqOVjUlFQj63F1llUURb1RSpKh5WVTyklY3tav7rA5p69fcUeOCv8k2bJl1xBU8WxpUBJ9EXLVqkGevpKzxt2jQtXrx4qI4LwChhg+6ta0rWSUTbpiXN7Zb8zujpRXXKZT21pbOaU1OiGRURTSqOaGFts0usl0b9eqUj+16Fe0g7Ti1XNBxQQ3vK9WCL26C7I626TMIlyBvak0pYpLeEu8+n/1uwWs+GAnq3IaE1TbakzK8dp1eorbVDE//3Wm37i1u1fJe9VBaUKvpI9G/sbuVAoSN2A+PXQAbg+T1GbBLbVpplc54ml8dUUxpRSyLt4nRjW0bltjqtIqaWZEb3v7hcr6xqVjYjVRaHNLu6WDtNj3T2Y43Jfaypjrhq9OY2i+cZt1LMrrdM+oSIX5NvvUblN16rNe/bW5UhaYq9z0hlXAsZJrwx3hG7AfQVr3vsW+a3VWFB17bUJrxt5Zcl0l9c1ujaszy3pNGtCGtLpvXWqmatbcsonc24PUoq40GFgn5l0jmtSqSUzUmxSNAlyj15blxfHA0qme6ciG/uSGtZbbO2+NE12vZXt6lu933UsKxeE6IlxGyMOwNOosdiMZck7y+Rbt+z2wAYm61dbJDdvarNkt0rmhJaXNeq5Y0JRYN+9z2rSLMZ8eeWNLjBt+fZhmSt6kjn3CDd75OeW1yvPWZVqiWZdUn4sD+lgN+vVS0JtSazLpC/taZz4D+pPK4VDR1KZnNumblVo9uStNiyd/XxGy7SlLde0UtfPE+LzzhTW08q1eTyeFcwt+O0Nw/Wx21FU0fn5ilUo2McIXYD49fABuCdbVlWNXcok/UU8vvUkOzcZHRCSUwzq4v06rJGd38Wr60i/aVlDVrbllY85FNLMqnEe5Xmkk+eospk5ZaJx8JB1bVm9OqKBvezFbGQ3udr1j7fPk3lr87Xmm98Sy+f8DmFmhJuFZqtVrOfmVQWY8Ib4xqxG0D3eG0LqZPpnDwvreqisOt7bm1bcl5O7emsaluTCtiK8FTGJdInlUY6q9JDAdeqbXFti1a3pNzGoclMVu3JtMriUdWUhNWW8RT1ybV5CYcCqioKaWVz594l9jtT2RZ1vPm2PnPrtzV94Wv692lf0ZMfPUPzfHE11bZSpIZxZ8BJ9D333FM///nPtf/++/f5/Z/97GfaY489hvLYAIwS+eS5VYhZz7V4KOAS5aWRoLafUqqFa5q1pjmlkljIJavXNidV25ZWKOB3FWhvdyRdD1UbYluVm/VXtYoz22DMkuIWsHeZUaF0Ou1avORyUl1Lwi1Bs0BvPxn0+yW/382yW6K9dtlChRrW6u5rfqayAw9wg++yeGfbmZVNHe6NhyUHOtJZvbm6RdbExZLpDM4xnhC7gfGrd8I8v5rMKtLbkik3Gf74wlqtbU0pHPSrvj2pqRUx97nf51c87HP9UmtKIopFAvJynt6yBHtHxg2u12aleMQn+89bllPW86m5NaJJFcXqSKXVnsopk+1cbZbI5FzbtpYV78i/dq0eueO3atppN61e2+HidDKd0YzKcrUn0i6hz4Q3xjNiN4B8vLYJcEtmW/tUGxtbQn1CcUQ+X8btPzazMq50xorS/JpWHtX/vV2n1c0d7+1zklNjW1qe51dbMuPaquVsytvLKeclFfV7Sqayyvl96shktaIhp0klYRWHfGoP+V1rN1tJNmHRuwo3Nui7X79FtdvvotJU1k2c2wrx4khnkZqhjSrGgwEn0c8991wdeuihbhPR8847TxMnTnTXr169WldffbV+8pOf6O9///twHiuAEU6eW+LbepXPmlDslnUlMzmFAz5NLLXg3ab6lqRakmm1JtNq6bDFYJ2BOuy3BLbUnOy8zirKX1ja1DVItgDf0pFxLV5SmZyW1be52fBY0O+Wk7nkezarTEuzPvHcX/Tz9x+jJ6dtry9886fabYsaTWlNqCQa0rL6DrWlW12SPxDwa8vqIk0qi6o9mdGUirhLBuSXrgPjAbEbGL+6D8C7t2Oz1WUNbUk9uqBWb69ucQnuHaaWKZHJKJOTZlUXu4nylkRWr62ocxuETyuPuSXjzYmEi9OeBXeL8VlP7amMljdatZqnVCajd+qTbhOyUDCgxo6sYqkOHffPe/XgYcdryQ7v0123/UmhWFTNq1q0tjXp9j2pS2b06vImTSqPqagpoUmlVKNj/CJ2A8iPxZva0y6RbbF39oRiLaprU31rUhVFERd/bY9Pm7S2Vdxvr2nWqqaEWxluFepWmJbN5eRXziXfrQ2bJdI9v20AnlNtW8rtW5L1rMrd71aR29fbTirTjIg06093687dj9ELM+bptK/dqRkTS3TAVlVa05zU26ub3T4q8fditqGNKsaDASfRDzzwQN100006++yz9cMf/lClpaXujXhTU5NCoZBuvPFGHXTQQcN7tABGpJ+qBW5LnttmYG+sbNZba1pcZbhVq9mSr9JYUDWlUa1pS7rlYU0dnmx+Os8CeDLdmQzPp6+tmtyS2sWRoBt422Zj0YCUzHhqTWRcoG9st1F6TmVxn+atWayLf32FJjbX6qktdtYC35ZKpgOaVN+qynjIJeLr2xLud+25RbXbSdw2SrOgbr/L3nhYYr1rV3FgHCB2A+OXvU931WHtna1d7GM+kW6bkS2qbXNVbTYpvrC2RdtMLHFV5W+tbnaxPJPztLC2TSWxsAI+n2v1ksjI3d7am1ucz9jdNqU7f586Ny3zKekm2G0yfPKiN/S1n3xHVQ1rtOJ9e+v18BbKZbOqLvW0srHDxeata4pUFg2pLB7WTtPK3Qo1JrwxnhG7gfGpe+tUqz63lqg2RrZCNttnzOK37V9i8diq0N9c2arFa9vV1GGry5KKBPxuhbetALOEum0oWhwPK+N5btV20Jd1G4zaeLg9k5EnvzK5nBIpq1vPubarrYmsci8+rzN/dYUq6tfoPzN21IottpM/6Ne0iribnLfV3jZK33pCsZsMt41Mo6Gga6NqOYHue64AY82gskmf//znddRRR+mee+7R22+/7f6Rz5kzRx//+MfdxqIAxmY/1SllMRcMVzYl1J7MuqR01J/VisaM6poDLsDbJqNrbaPRZGf1eXcpG1zn/vu53y5ZyVaCZXMZV9Fms95WTZ7J2Gy43mv/Yp3bPH3iyfv1lb//WIsnTNenPnOdXi6dLs9m0pNZ1bWlXQsZ+xlLyLelcqpc3aItakp6LF3vXokHjCfEbmD86m+DbZt4bkqk1NyecR9tMzFbtbWssV3v1LaptjmhZFYK+jwFQ0nVNyc0pSKqlL0v8DoT5ib3XkzPvPf1qqaUwpa3TzXquCf/pHMeulPLJs7QhRfcobcrZmhtY0JLGxIqjbVrekVcpdGwe19hq8bKY2GXQGfCGyB2A+M1Zluxmk1Wr2qy5HjGJayLwkEFAz7X1rQoEtLC2lY9926D3q1v1avLm10BmhWrTSq1SW+/XlnepPZkTulcVunGhMKBgEu6+3xZt+Ksvi3jJsJDPmv10hnTU54UzHn6+JN/1Nce+rGWTZqhU79wo94onyJ/c4emVRSpLBZyk99b1ZS4j/VtKa1sSbqVan5/xt1P9xZywFg06Ff21KlTdc455wzP0QAYlf1UbfbbNgRrbE8q4+UUDfi0uiWpBaublUzm1GzLwGy52HruywK7ug26vfcuHVnJbXmSlVra0y6K2yy4bXBmPvD20/r6327Vz3f/sH506Olq8YXkz0iWC/cHpNrmDlUWR92bi3hYKovZpmQRt+lKe6qzOs4S5xU+ZsIxfhG7gfE9GV5dHNai2la9lEi5arG61oTbbNtapSXStrw7p/+8Xasl9e1KZTx1ZDpjusXnbIcl21Nq7kiqI2OD8p4xPZ9ANxZ102np/a8/qW88cJuL3TcfdrrSCinX2OY2OktnsmpN+NzAvaI4pOJYUJPLYu79hg3Qbe8Si+XWd713KxpgPCF2A+OLxTxLoLensm5Semm9JdRz6kinVFkUVU2xtSwNuonu1mROa1vatarRxuedMbupo0OxoBTwy12Xy8olzX2yzUSzbhK8c3TcaW2icyLcoqt9PHTh07rggVv1230+qp8d/Rmtssy6W03uk9+X02pr8dqUUCQYUCQUUDaXVWk42NmWrTXl9iezOE7hGsYypocA9LuUzJLQloy2gG6zykvr2/XK0iZXtdbUbj3SLdndOXM9WN1/pHNrks7rLBkfDkgzGlbonYopemjLPXTMyT/Qki22VTzsV5EN3hMZN4j3W8V6IOCS+9ZnvbIoqO0ml2l6RZHq2lLvVd4l2TUcADCml333l2jOT4Yvrmtz/VJt4223kahfakmk3DJuG4i/uKxZyUxGjYmeAT0/Od5mmfL2jEL+dVebdTezYYXerZiih9+L3a9O2cbdia1Gsw3K2hI5RUI+TSgNKZPNKRIIaGZlkbasKdGa5oR7M2CPp7E9pdrWpEu0d6+gBwBgrLKYbWPaBauaVdvUqgWr2rS2LaFszqdUplklkaD8AZ/SaU+RiN+1Rk16PWN2qxWbBeTGyun3vmeT2/3JvRe7l1RM0T+22kMnnv4DLd1qnttwtC3lucS7jdJXNidVXZpyq7+t73o45FcoEFBzKqF317arNB7WxJKoex/Su4UcMJbYhBMA9Ln8e1lDws0qN7en9U5dq96ubVNjIu2WjHWkO4PzxiTQ++I2HLXlZNmMznzk5/r7HV/Q+5a+4paHvThlG1clVxQJKxLwqboooljQp4pYWKXRoFoSadf/fPspZdpnq2pFQn6XQJ9QEnEfXSAHAGAMycfqpfUd7qN93Vtp1HqgSovrml0l+tK1bWrqSLvl1yuaklqytt1NRDe0ptXWfSTeB0ukt1hPtj4Esxl97d8/1z/v+IJ27xa77YhsAG690qNhnwv2NiEelOcq5WyJuSXMF65p0ermhNtc9LUVzXpjZZOrxiOOAwDGysS3rbBa0djuPtrXvVnSuTgW0vLGDr20okVvrWlTbVtWjR0ZtaSluraMVjWlVdue0bKGlFa39r0OvD373wT6+nSP3TbuTsunFyZto3g0qHAwoHDAr3gooOJw0MVxez+xqLZFi9e2uBVqlUVhTSwOKxiQW91m8dza0azvfQlQ6KhEB9Dv8m8bvNqge0l9m95c2aKFa1rVns65AGozcLaJ5/oq0gZrWtNqXf+n72unlQv0g/1O0nNTt3XXuyVmgYB8Ps9VolXEI1rW4HPL0K0n+sSSiCLBoNa2Jlzw7nyTklRrIq3SWJiebACAMR2re2/ilR+sP7+kXo+/XadVzQk3GW4bldlEdGNHUq0dWTeBnbbl3u8luzckO4jYbaK2pNw2F3dLyj0FglIoGJTf59e0ymI1tCcUbvK5NjF2AFn53MDdc43Xc/RWBQCMvT1KfD7XZs0Kv7q3PrHbJGwz0EhA6XTWxVy3Uvu9+xiq4rX1xe6OnLS8oUPlcRtDB5TMeIqEffLnfKprTymxskmVxTEFA34llze524WCPjUnMm78bddvMaGYzUUxZpFEB7AO28CkPZXRG6uS7qMlq0NBv7K5nFo60m5AbNbXA32wdl7xpn52z7fVHCnSJ068Ss93G4Rb0t76o9qO4z6/tKbFlrXlNLEsrsa2lOKRgBIZm5FP6h+vr9HE0oiKIkGlsjn3BoXNRAEAY02+VUt+Ey/bNMwmvq2y3Kaf313bpicX1mlhXZvCVkLmSclUTmu9lJKZtNrT1iO1Z3/UoYzdxt4vRAKdSXSrVCuNBBUL+Tqr0uWpqSOraVVWvZZWaTSkquKIazczqTSiuZNK10kwAAAwForUbG+SyqJIV8syY0n2tlTOrQRf3Zrq0f50KK0vdtvvTNqK80xWsUhQ2VxGyvkUCfuVtU9DfjV1pFQWrVBlScRVoNvktz0uK2Cz8Tebi2IsI4kOoEcPdNtQrD2ZVksiI78816/UNhZ9Z02z6lvTSg5l6blxW4L79Gb1TP16x8P0o70/qeZo5xuJPBv7ux3G05YosEDtU0k0LJ9ymlFdrIpY0FWxbVET14rGpKIhn3aYVu4CuA3A6cUGABhrbHLZ9i1paE+6FVvPLlqr+cuaXZLaNv6yijDP86mlI6WWRFbxcMBt5GntXLyclEhtQgJ9ALHbWIV7MCfFwhaxLZmeU2N7RiWxsOpaEgoEAiqLBNWayqg4GnSD8UhZRNtMKtWs6iLiNwBgzE18p7M5hQL+HivJTDabU4X1EbcxuO1dMtQHMcDYbe3WWlJZ5eRTcSSkaDjoWrGls56rTrfjrWtLas7kUk0ojri9yOxx2ArwvirsgXGXRK+oqBjwm9j6+vpNPSYAm1l9a1L/WbhWKxvb1Z72NLks7JLp86aWaW1rUk2JrNa2Z5QY4gT6tmve0RUP3KhzjjpXiyqn6vKDPr3ObSz0xsJWU5dTOutTJpVSIuBX0O9TLBTVTtNKFQ8H9caqZr26vFmprOeCuW1QFgh0BnBgPCJ2A2ObTX4vXtvmNuBcUd+uZY0drrJtcmlc79S1qSWZVjrjKZXOya+cS6Sn2zy1JXOuHVs/7c2HJHZ35/Y7yXgKhd5rz+b3uT6rsXBA0WBQoYBPW08s0eyqIkXDgX43SQXGA2I3MDZZXLOKc0tA17yXPLeKdEuo29dlsZBbAf7Monq1JDPy+Wz/kJybjB4KA43dAVuV7sbePlXEwyqLhdWcTKokEnYT30G/NLEspvJY2I25Z1bFXUuX9W1yDowlA8ouXXfddV2fr127VpdddpkOP/xw7bXXXu66J554Qg8++KC+9a1vDd+RAhg2tnRs/tJGZWz5VVtS1UWVbinZ04vq1JHKaVl9mxIpq0wfohYunqdTnr9fFz5ypxZWTVvvTa1KzjYxbU1k5bcNTYKdPVZbk52brDR1ZNwu4TE3Qx5QwOdXcTTg3ohMsgDPDDjGKWI3MLataurQG6ta1JFI69VVTcplpfZUTi8sqXP9za3KzarO/dm0rBfaqvbcZovdeTaMtphdVhRU2B9w1W2BkF/NiZRm+YtUVRLV5PKYdphWwcAbIHYDY5Yllt3eXgorl8u5+LyquUNF4ZBrVWpj16JwUIl01rV9Sdts91AYQOx2e51ZvPZJPsuiy9q32Oaind+cVl6kXWaUanJ5kdpSGc2sKlbI2siF/PL7/V2PCxgPfF5f2wKvx8c+9jEdeOCBOuuss3pc/6Mf/Uj/+Mc/9Ic//EGjVXNzs8rKytTU1KTS0tKRPhxg1Pj3m6v1yIJalUVDenNlqyaUBl0/NOt/vqY56ZZcL6pLbFLf1LyK9iZd/bfrdejbT+vO3T6sqz5wmpLBDQfdiE+KhOQS6cWRsMqLIq5H2+6zqrX1pBItWdvm3oRsUVPskunbTSnVlPL4EBwxUPhxitgNjD1PLqzVn+evdC3YXlvZrIDfUzQYVjKVUl17Wm2JjBKZzpYqIxW7LYFeHPZpy4nFStnmolnvvUG3tMfsalfFtscWVdp2ctkQHCUwMojdm++cAGOBbfz91Dt1WtmUVEU86OJ1LNLZLmXBykY9826T3lrZqg5v88Zu+04g1JlMtwK1mtKwqkvjOnjbiZpVVaTyorArbGvuSLkKelqvoZBtbJwadJ8Dqzi/6qqr1rn+iCOO0Pnnnz/YuwMwCkyriGtSaVT1bSkVRfxus862VFbv1rW5HmetiaFbSlac6tCcuiU642Pf1j+32mPAP+d2Js9JRdGgSuMhtwzcdgu3fmyTEzFtMaFEwYDPJdBLYyHauADdELuBwmd1L/VtSb29utVtpl3XnFR7MqW6VtsEPK2o369EqkMB+dTckVEi2xk7RzJ2F4V92n56ubasjmt5k/Vu9ymdyammLKrp5TFNKItpYkl0ncdprWpYGo7xjtgNjM39x2wvk0Q6p8p4SC8vb9Tba1rkt6VauZxWNyX05ur2IRl7DyZ2WwF6JNy5X0ou4nOrz23y2/YxWbC6RRNLo9qmJKpwIKXXVjQqmcm63IE9pinlRawmw7gx6CR6VVWV/vjHP+prX/taj+vtOvsegMIL6rZ8bIvquBLJtJr9OS1tTKghaUvMErJ9TjY1iAdyWZ3xzB/1y52P0NLySTros7cp63drxQbM53VucjKjKqoZVcXKZqXqkqiiQZ9mVMa15xaV8vv8SthO4mxkAvRA7AYKO05b1dpba1r0wrsNLkaH/XLLv20DUeuhmkhLaX/WVYZZz/PkECTQhyJ226T2VjUlmllVpFUta1UaDcvzctpjdpX2mF2peDi0Tts1SzRYmzlbzm79062HrC0VB8YbYjcwNuTjWlNbUm/VtqqlI6Pa5naFg34tXtOqpQ3tSuV8SqYzak96mzT23tjYbUnz9pQUCdol2Pl+IpVRJBTS6saEi8kWr99a3aIlDR2uXdtLy5qVTGc1b7pHrMa4Megk+qWXXqrPfOYz+te//qU999zTXffUU0/pgQce0B133DEcxwhgGAfmi+va9MbKJr1T2+Y2IlvTktSba5qVykmZ9KYPwqc1rdb1f/q+dlq5QO9UTdXDW+056EG4sR8JBQPy+4PaelK5GttTCr3Xgy0eCSrgD7jP8zP9K5s62NwEeA+xGyhcFtNeXNaoV5Y1asGqFvneW2Ztn7cm067NmS0Btwpv600aCfhc25TMCMduG2RY67WWREbL1ra5Hu1VxUEFfAG3fD2fQO+9AZlVoNtgfcJ7G6/Z1/RaxXhE7AbGhnxcs77jry5v0uqmDtW1WEuUrFI5Ty3tGaXfa4E+krHbonFJNKiyeNBtTF5RHFEk6FMw6NPyxg69uarFtXntSGYUDPhd/3ZbHW6PjViN8WLQSfTTTjtN2267rW644Qb97ne/c9fZ14899lhXUh1A4QzMbTnW80sa9fbqZpdIt82+kmm3mszNSG/KRqIfeuMxXfnAjWqOFOkTJ16l56duO+j7KApasiDgEubWR3ViSUTzppRqWUOHUtms5k0pdwE8H7ipYAPWRewGCpfFNxuwVhZFVFGS1KI1rVqyttUl0C0xbf1JbVm1l5WyvqxiQdvA7L0+aCMUu+3Xx8JyPc+t0s6WqVcWhdWezKkk5ldDe0ZPvlOnuZNKNau6qEci3VaTWQW6JdDto30NjEfEbmBscHHN59Nbq5r19qpGrW3PqKUj6xLn2W6bew6+zGxoY7cdi1WhTyiKaEp51L2NsPYzW04ocaveFq1tU1Mio0zOU2sy5fY3sUl7YjXGk416V2rJ8rvvvnvojwbAZh+YN7SltWBVk15b2aLG9uwmJc2727r2Xd38xyt1/9z99M3Dz1RztHjQ91EUkCrjAcWjIYUDQUXDVnkeUUcq6yrULLAnMzkFAv6uQTYVbEDfiN1AYbL4FosEVduSUFk46HqodtgS66DfDWo70jmXNC+JBVTbnlVHShu9EfhQxG5rzmIdWiaUxFQcCaotmVE6nVFJNKK1HQnFQ9a1XVrleqQ3qzwe7tGuxarTrYVL957owHhF7AYKn8Wx6uKwMtmc0l5AzYmkku9VnufZl72u2qyxO78ZeDTgqaworF1mVqipPaPGREbZbFZZf1BFoYDqclnNrIpremVcrR0ZbTOpSFtMKCZWY9zYqCT6woULddddd+mdd97Rddddp5qaGv3tb3/TjBkztP322w/9UQIYFjY4bUumtby+w1WyDUUCfWbDCi0pn6S3JszUR07+geZPnvNeSdzghUJ+VZbEVBWPqKzIEul+zZ1cosllUU0si7oNynr3QKeCDegbsRsoTBbfdp5WrqJwQM8trpfns805PbVb8lw+xcI+NXV4as90RvH0CMZuG1jEI1J5LNJZOV8c1uK6drUmM4qGOo8s5E+qqCmkKeUxt7Ha4roWW0De1drFLpZUp4ULQOwGxgKLa7aiuqYsprJIQKv9cnuXbKqhiN2+96rgg34pFg2oLZ1TQ2vStUq1xnCpdNbtleaK19JZxSMBBQMBFYWDmlwedwl09i3BeGL/Xgbl0Ucf1Q477OD6oN93331qbW1118+fP18XX3yxhttNN92kWbNmKRqNupn5p59+eth/JzBW2YDVWqU0J/7bh22jeZ5Oee7P+vuPz9THX37YXTV/yjYbnUCPB+Vmu7PZjKqKw25ZWVVRROFAQO/Wt6u5I+MC9pTyuPuYXwqer2CbXhlzH6lgA4jdQCGz+FZZHHFVX7bBtm2sHQzaEuusktmsEilv4yfBhzh2VxcF3GR20PYxCUi1LSllslk3AWCV87Y83DYDL7eeM57UksxobVvnhmvWjg3AfzHuBsbO5uBN7Wl5Nt72eZu859hQxm5rIWObiZbEggr5fIqHA26y/qVljaptTiqTk9KeT40dadW3p7TdlDJtUR1XZVHnmJuxNsabQSfRzz//fF122WV66KGHFA7/d+nlQQcdpCeffFLD6Te/+Y2++tWvumT9888/r5122kmHH3641qxZM6y/FxiLcjmr/mrTioY2ZXLZjV4+Ziram3TH776r7/zjNrcT+J+2O2CTjs3Cf0U8pPaMbRKadbuY2w7h0yrirueaLQF/c1VznwPufAVb7+Q6MJ4Ru4HCZ8lpz5NWN3doTXNKHSlPzQmpPTs6YrcNxJM5T8l0TqFAQBNKY26z03g4qKJI0G0CPqE4qm2nlGmvLSdoZlXMrSybXV3UtSkZgP8idgOFnzx/Y1WzXlzaoKaOtKvk9svrTKZrlMRun1xCfFJpWFMrYtpxWrm2qilxK8Aj4YASadvA3NOk8rhKoyG1pbJu0/BZ1Z0ryGw8vqKx3T1We8zAWDfoJPrLL7+sY489dp3rraVLXV2dhtO1116rz372szr99NO13Xbb6dZbb1U8Htedd945rL8XGIveWdOqXz/9rv61oFYtyU3bBfxvd31Juy1/Q5/+2Ld06SGfVzL43wm2jVEatj9OniJ+aYep5YqFAqooCrmkeH1bWpXFYbeZGgNuYGCI3UDhs8HqDlPLXExMJG1jr43vnzocsdsqzyeXhrXdlFLNmVikWVVx14LtyJ2map+tq3XothN1xA6TtMuMCreZqA3Ay+Jh1bXa5mRsIAr0RuwGCpcll22VlY25361r19q2lN5a06RlDYmNbuUyHLE76dkeJWmF/D5tVVPsEuFrmjqUzmZVHPFrclncbRI+uSSiuZNLNbuqqKsCPf8Yl9Z3sKIM48age6KXl5dr5cqVmj17do/rX3jhBU2dOlXDJZVK6bnnntMFF1zQdZ3f79chhxyiJ554os+fSSaT7pLX3Nw8bMcHFJrn3l2rJ99pUGNHZuOWgdtMs8+n5aUTdN+8g/WzXY/U6pLqTT4u62xeEQtpi5pS1banVNuWUijo15Y1pS5w+3zNLoFeGgt3bSYKYP2I3UDhs4GtxWzrL96RyY2q2F0akqtY8/kCmloZ1y4zKrVFdZHarH9qKKCJZZ0t1thAFBg4YjdQuKzYK5vNqTga1H/eqtN/3k5odVO76ts2Yh+yYYrdebYOLJvzuepzi9uWZ2tKZFQWzbpVZbYpajQc0OTSqCaWRrv2MLHHaCvJJpREVNuSdF+znwnGukFXon/qU5/SN77xDa1atcr9w7GWEP/5z3907rnn6pRTThmeo5RclbvtCjxx4sQe19vXdix9ueKKK1RWVtZ1mT59+rAdH1BI7N/twro21bW0K/3eRmSDMbVpjX7zy/O117vz5fn8+v4Bpw5JII/Yv+nSkKZXFemgudU6aofJ2mNWuXadUaEZlTG3E/ges6vcZmTF0YBLKLBsDNgwYjdQ+N5d264XltYrk8u5KvTRErtNMt3Zim1aRUyHbjdRh203UbvPrtQu0ys0oyquLauLXLzuvuSb9mvA+hG7gcJlxV7t6axeW9GsutaEVjR0aEVTh1KjKHZ3Twqmc57bVHRCUVRbTyqWdViztxqJTFbtmZxWNCX09po2PbVorWsJa3HcHqOtJLMEOivKMF4MOol++eWXa+7cuS4hbZuKWluV/fffX3vvvbcuuugijSZWtd7U1NR1Wbp06UgfEjBqBuIt1k/c51drYnC9yz74xmNuGdnU5lolgpb23nRWT+5qyn3S1PKo65FaUWwBvFSTyotUUxpVXVtaTR0ZN+huTWXdxqI2EcBGZMCGEbuBwtfYnlQi7bk9Qga7GfhwxO48S577A1Iw4FN5zK9wMKCOdNbFbKtWsz1KLHZbzGbJNzBwxG6gcFn8s8rtkmhQUyriak5ktLZ95MfdfSkJ+1RVFNL0ipgmlUclz6fyopCKIiFFra1qLKSWjowaE6kee5PZY7RVZtMrO1ebsckoxoNB90KwzUTvuOMOffvb33Z92iyRvssuu2jrrbfWcKqurlYgENDq1at7XG9fT5o0qc+fiUQi7gLgv2zWeEl9q7Kep6qioOpakxrImrJoOqFvP/y/OmH+A7p/m331zSPOUnO0eEhObX4rsYBfere+XUXRsOvLFg/6FQn6VVkcccvh2lPp91bAsGwMGAxiN1D4K8haklmtbUmooS094F7owxm7A+9V48QjFr99bjPR1S05/e3lVXr/FlWurUu+hQtLvoHBI3YDhcvGrDWlEQX8fhe707mcS76lRzB2W9wO+qSg3ya+LTEghUM+bTOpVNtNLtWuM8oVDQfdBqjWOjUc9GlNS0qZbE45eWpLZjW1It61N1mFL+xiPC1cMJ4MuhL9O9/5jtrb210l+oc+9CF94hOfcAn0jo4O973hfBOx22676eGHH+4xoLCv99prr2H7vcBYY8uo317dpqWNHa63qgXRgQjlstpt+Wv6xhFf0lkf+caQDcKtB7qJB6SJxZ2z3daHLZXzVNuacse4rL5DK1uSSmVYNgZsDGI3UPgryN6pbZHP71Mqm3HV3yMZu61EpTwWUFnMr6DP2qtJgYBfE0usT6oN0D01t6e0uK7Fve+IBi22s+QbGAxiN1DYmtrTau5IufiY87wBJdCHK3ZbztxidHlRUBPLoppYEtWcKaU6dNtJev+WExQJBvXm6ma9vaZVSxva3STA3Eml2nl6hdswfL+tJmh2dVzF4QB7k2Fc83mDbChs1eC2sWhNTU2P69euXeuus77lw+U3v/mNTj31VN12223aY489dN111+mee+7RG2+8sU6v9L7YxqLWG91au5SWlg7bcQKjlf1zf/qdtfr7qytU25rWglVNWt7QoZb+Irrn6cQX/6Z/bfE+LS+rUSCXVdbWbA8R20s8HpUSWSngSVUlEcUjQe0yrUwf3GGq3l3bqnTW07aTS9WWymrbySVuWbgtH7PZb+vDlt/YBBgLhitOEbuBwvbCu2v1+MJ6pdJp/fbZ5Vresp6uqsMUuy3SRoOdy1gnWSWaX0qkcwoEXEMXFUd8mjelXJFwULOri1y1vC1lL4uHXU/0/CZkxG6MNcTuzXdOgEKL3U+8U69EKqvfP79ES5vWk0YfxnG3jbmL40HNLIvKH/Ap58ntrxIN+jV3Sqlqm9NqS6UVCwcVDfkVD4fcHidH7jhFsycU/3dzc8bgGEM2Nk4Nup1LfiOg3ubPn6/KykoNp09+8pOqra11rWRsM9Gdd95ZDzzwwIAS6ACsn2pa79a3qb49rfq2hFKZnLU861N5R7Ou/tsNOuytJ3XxIZ/XT3c7ekgT6FFbRuaX2wV8ywlFymZymlAadTPeE8tjWlTXpo50ztq2uwS6DcItoOc3ImPZGDBwxG6gsJXFwmpNpvXGiiYlUv0n0Iczdk8s8quqNKqq4ojblNwvn5IZT5PL46qIB1UWDWve1BJNLC9SNpdTQ3vaJdPrWlNuYzKbBCd2AwNH7AYKW3k84tqQLljd7MawYZ+U8jb/uLumLKyg36+M5yno86upI62iaFD17Rk9vahe2Zzn+p8nUjmFQj7tOqPIrR6zvVikziQ6Y3BgkEn0iooK9w/HLnPmzOmRSLfqc+uN/oUvfEHD7ayzznIXAINnFWBBv88NbheuaVNjW1odfTRW3XPJy/rh/T9QLJ3UZz76Lf1j6z2H9HTbbHhFcUiRQEDVJRHNqIprenlMe289QVvXlGhVc4cW1bZpcnlM9a1Jt9HJrGo2KwEGi9gNjA1lsZCq4mE1tqfU2k8h23DGbovboWBA5fGwJhVHlcrlNHdyqda2peVlc6oujWnOxBLtMqPCTXS71nG1rS6BbgNxqz4HMDDEbmBsmFEZ047Ty7WorsVVePu8zT/uNhG/NKEsqsp4UFah1pZIKx4KKJPJqi2ZUTAQUEsio1jYrwnxqMv1lURDbhIAQE8DfkdrrVNsNvyMM87QpZde6sreu/crnzVrFr3JgVHOBrGZnKe65pSSmZzSfSTQSxOt+vF939HLk7bSV476mlaXVG/y77W26zbzbiu+g0GfKovDKouGtM2kEu03p0aNibS2nlCi3WdVuqBts+O2SZkNwsviEZdAt0E5gMEhdgNjQzKbU3Es5PqQpjZT7M6z6FtjfVRjYVVEw5pYHpdPnqZVFGnOxICKwkGVxkOuv6q1WDP20TYV7d6+BcDAELuBsaE5kVUk6Ne00qieTGaU3YyxuzvbCHSricVuQn5lQ4cmlcfdanDPszcUnQlz2zx0UmlEh86bpJmVcVUURTWzKk4bF2Bjk+jWi9zMnj1be++9t0Ih3gwDhcYGsTMri1ReHFZ4rV/ZbqF8SvMa1cdK3cYlnzzhSr0+YZZyQ7SMzPqmhkNSsf3PJ9UUR7TlxFLtMLVM1SVRNyC3wbZtKGqJ89rWpIIBv1JW3VYcZvANbCRiNzA2WCLa75PbrLO74YzdtuY0HJC2qSlSeVFE4aBPkyviak1kNKs6ri1rijWpNNbn3iQs+wY2HrEbKGz5/uG2ufbSta16bXWz6loyymyG2N1XMZtNxLdYC5eIVZeHtMvMCvdxwapWLaxrVXN7WlXFMe23dbUOmDNRlcWRrsewqqlDK5sTrnLdNki1MTvFbRjPBr228oADDuj6PJFIKNWrLyMbhwCjlw1qbRZ6l+nlWr62TWta0rJVZR984zFd+cCN+sUuH9L3DzhVr07cckj/yAQD1v/cr4BfrgL9fbOr3GYl1SVhN0C3QFwW6/xzZFVrtt3xFhOKVduSVMSWvrFxKLBJiN1AYbPB7pZVcTcQzq8GH6rYbQNsq0UrCkj+oNSS/O/y76qisHacVqaa0pjSOU81pVE32W37l9glH5/ZcAwYesRuoDBZ8tlamlly+omFa/XM4kYl3wvewznutjS8/ZpcrxhvyW9r15KobdXUipjbZ8UmwquLo3rf7Eq9sapF08pjev8WVV0J8vxjqG1JqK4lqR2mlbv91Gyszv4mGM8GnURvb2/X17/+dd1zzz1au3btOt+3/ugARi+ffG6X7XnTSrVk6Vqd94//1QnzH9BfttlHt+35sSH+XZ1V6JZEr4jbhiY+VZdG1JHK6LWVzYrXh/S+mRWy7cmsz6oFbVdt5/e5BDp9VIGhQewGCpslqV9f1eIq0aPphL798KbHbrc/SVFAlUUhtSRyLl77/D6VhnPy/D5NLYupIh7SNpNLtXVNqda0JBUI+DStIq6tJ5b0mODOD7ZtAzWL3VSqAZuO2A0UJks0Wzy0dijtqZzS2ZyK0gldOASxuy826R0PS5mMlMhKuW691yNBy9F5ioYDCvn9rsq8vj2tlo6MgsGAWwm+68zKdeJ2/jFMKYu5cfmKhnbVlMXY4wTj3qCT6Oedd54eeeQR3XLLLTr55JN10003afny5brtttt05ZVXjvsTCox2iUxWxZGQZkV9uvenX9XUxtU6//Cz9OudDrdS9SH9XRa/bfl5Nie1p7IqjQWVynh6t75dsXBQK5uT2mZisevxmp/Vpo8qMPSI3UBhW1zXpifeqZe/rV1/+ulXNb1p02K3DZOnV0e1RVVcs6qL5MmnCcURLaxtUzqT0dr2jOvjOqUirtJo2P0O68k+uTSqSWWdLVy6V583taeVzeZcpboNtqlUAzYdsRsoTPmisMVr2xW3zTqV0u1DELv7qjwPB6XyaFCza+JavjahVS2pzkG4JNtHdKuaYmU9T7GQ38Vz298k53kqiQU1uSze794l+ceQSGc1ozKuSWXRrhZuwHg26CT6n//8Z/3sZz/TBz7wAZ1++unab7/9tNVWW2nmzJm6++67deKJJw7PkQLYJPnBbkNLQq8sa9TjKzvUOO8gPbjlHnq7esaQn117W2AxtizidzPi1qPF+rBZFXo667nNSta2tWtJfYemVxV3zWrTRxUYesRuoLAtrW91ye1UUVy/2+Eg/WMTY3dJzO+S3lMr4zp428la05pym4VOLi9SJOBp4dp2twQ8EgooHg50JcfL4qGuSjVr65KvPm9LZVzcZxUZMHSI3UBhyheFLapt1o5Ty9TYPlF/3eVgPTBzd70xhONuqzIPBwOuRcsWlcVKJnNqTqZV4vcp5+Vc0rwoElRlSUxbVhepojiqkkhA8WjIJcQtnvfXmqWvwjZarAIbkUSvr6/XFlts0dX/3L42++67r774xS9yToFRyhLoixYs0aRzzlR41i5avN0heuz9H1e623KvTeF7bzbcNkwJ+6VoyD76FY+ElexIK+v51NCaUlk8qFg4oFwuq6qiiLas7txUlFltYPgQu4ECtnattj3zdB0wc1ct3eEw3bXXx90Kr015828FcDaI3mFKmbaeVKKOZU3qSGcVCwVUUxJWIBhUcSSoVU0JZTyvz+R4fqn3hJKIvGbPJdjLYqE+K9oADB6xGyhMrigs0aLwF09Ty3Z76q9bH6if7nOcEolNCN69RCwfFw2qOBrSlLKoiqJh7TijSo2prHLZzlYy0aDfTZbvP6fGrS6zXugWqwcSpylsA/pmRSaDYgn0RYsWuc/nzp3reqPnZ8rLy8sHe3cANpPsvx7R9h86QFUvPK2VsQp1pHJuA89NZcPp8og0uzKiaRVh2f6gQb8tAQupqjiqUMAnn2ez5H558jSrKq65k0pcP7ata4q13ZRyNwvOzDYwfIjdQIF69FFp551V+fzTCk2bqpmVUU0vj2pqeUiTi4ODr4axie6QVaJHXJ9Ta9eSzOQUDwfdRqH2sTWR1crmhF5f2eIq2my59/TK2DoT3t33MLFNy6yqbUp5nJgODBFiN1DYsTv21BNKTZjois0y2ZxSQ3DXUb9UFJLisYD8Ab+CAb9qyuIKBv2ubcs2E0s1qTyiiWVRTSyPaUZFXJFgQIFAwLVjI04Dm2bQ772thcv8+fPdbuHnn3++jj76aP3oRz9SOp3Wtddeu4mHA2DI2Q4j3/2uqi67TC2776W/feMqPb8yp+zaVmU3YfbN9TuXVByRyuJRTSyNuE1MbCAdsg1Ei8PauqZErYmMXlrZooBVvYVDbmfvmVXFrrdqWyKjqJWtAxhWxG6gMGO3d9llyuy9r9659ibVt4VVUdumOVMCWr62Tc2JrOKhjJrT6/64Ddjz8+T+bl9bJxZb2l0WC2pCcVSl71WOd9/Quygc0JRSq2oLujg9oSTsBt29sYcJMLyI3UBhxm5ddpm0335qvv1ONTQFlH7qXaUz2uixd15pqDP2FkcCioSCbtxdHA7qfTMqXML8tZVNmpaMasEqv5JpTwG/p3DQYn6oaz8TAJs5iX7OOed0fX7IIYfojTfe0HPPPef6ou+4446beDgAhm02/JJLlD37XM1rTuitF5ZrVXO7wr6ckoOsRreBeEm4c7PQsqKQAj6fakoj2n5qud6pbVUw4NMeW1Qrk/VUEglqeUO7KmJBt3RsRmWRtptSpmAw6JaAl8bDLrEOYHgRu4EC9OijSlz4bb1y6v+ooSOthjUNakvl1NJuWXOfOlIp9bcw3Ou23DsW9StnQdvnUzziV8DnV2kkqJrSsNvsuz2VVnVRWOGgz8Vk20PFfs+G4jRLvYHhRewGCnTcfeml0gUXqNTn08QFaxTw+za5hapF4pqyqFvpHbbqNEnTKuNuQnzGhCJtO7lMs6qLtaiuRaFAwG0eavua2Aai3fczAbBpNmYVaA+2oahdAIwy990nTZ0qvf/90sMPyxcIqMIqx0qiaklktLC2WY0djUoOIotu4TpfOB4JSSF/5xKyyaURbTelVNXFEdW2JV0lemU8rBlVcTcot8q2SMjvgvuciaXy+/397gQOYPgRu4HCiN0NLUnl6jtcAtxWdqWyWTV0ZJT1skpnpZRtRNJLLCC3+isQkCYUh5Xz/MrkPDfotpZqOfm091ZVqiqKalVTh1JZz8Vpa9dig2xLom/l8xGngVGG2A0URux2Adj2JGtNanFdm5t0Dgekjo0sRS8PSxXv7SWWyvrcCvCMJ00sjmje9DLXTi0/sS2VqKkjoyX17W4yPB4J9tjPBMCm2ah/Tc8884weeeQRrVmzRrlczxoYWroAI6y9XfrqV6XbbpPOPrszmL8XyI0F2DmTSrXlhDK9sqxFPmW6KtY2xPLnwYCUsoI22/VbPk0qiejw7ado55nlbsOSpfUdqm3pUE1pXFVFQdeDbecZlVrR1KHZVUWuF7oL8v3sBA5geBC7gcKL3flWKw1tKWVyOfl9fvm8nNJpT5FwQK2pnPxe5yS3iYelmtKoS6JHQwFNKIlqTXNKxbGA26NkzuRSJTNSaTyiaDigcMDvNga1Vi42uW2xmQpzYPQgdgOFO+5e3ZzQ6uak4tHggMbZpRG5CfJMVm61uMX2iF+KhkNufzGfL6BYxKeSWEhVRRFVl0S05YSSHlXmVqC207RyTSqNujuYWBKlaA0YyST65ZdfrosuukjbbLONJk6c2GMzQDYGBEbYK69In/qU9M470u23S5/5zDo3sQozs+PUYr2ytEiZVU1qSqnfJeHdA3vY/mL4fK7HWjwUUGk0oG2mlGqnGeWaWlHUOcj3Onukp3OeMjmf69Vmm5bZQN56sfF3Atj8iN1AYcbufN/xorDfrSJ7eXmDwqGAkrmcJsWirnVaY3tGfp8UCtgAPCQbvpfHQm5Zd3lRWKWxlGZUxlXbmlRpNKQpFUXaakKRYuGgS55bK7Z0Nqeakoh7j0CcBkYHYjdQ2ONuS2JbwnticVSVRSEt72sTk/dUF/ndCrHmRNptEBr0+RQN+tWczCrgt3F4UEURv6ZVFGtKeVRzJpcpmc661d69c3JWtGYXAKMgiX799dfrzjvv1GmnnTYMhwNgkzYyOeYYKRaTnn1W2m67Pm9mie4XlzZqSUNCxdGgplTEpKakWhI5l0jvL5keCUjxkN/tLG4dWEqiAQX8fpVFQyqy3i6Sq2KzZWP5qjZr5WKDf1q3ACOL2A0UZuzOV4VbMt0S3JYgt7i6rL5dPl9ni5bGjoxLpieskjwe1oQy2+w7qq0nlmjL6iI1JbLyydNWNSXasqZIk8viParS6tuSriK9rjWl8niYvqnAKEHsBgp73G1V4NtMKlXEYnV7Uh2pOtUn1r1deUSaW1OiRFYKhQOKBfyKhnyuZZu/LaniSEjxsF+lsYjrb14aDymVybliNVq1AKM8iW69jPfZZ5/hORoAg1dfL6XT0sSJ0p/+JM2e3RnQ+7G6JaEFq1vcYDmV81RTGlMoGNCyte1qS+fcbmR+v+Rl5TZAsflyG7SXxYKu53ko6FPWNhuLWjAPatsppV2D8fyyc0ug20fbjMwG/7RuAUYWsRso7NhtyXSLqcFwQEV+nyZXWH9Vn9I5nxK1LSoKh9WWTKskanuNRFzbF4vTO0yrcD/bfTK7e8WaVbBZn9XeLV0AjDxiN1DYsdti7uzqIvl9ObUmM2poT+nVFc2y/cFz3RJy08rj2nJSqVqTWbWncqouCSroC2hJQ7takhk1daTUnPKrvCiliWURV8BmPdZt4tvG6AA2n/e2CBzcLuE33XTT8BwNgMH597+lnXaSzjqr82ubBV9PIHc824gs4zYZs53Crb/avltN0A4zyt0mJRPKIiovCike9auyOKjyiE/lsYC2nFCkrSeVaOdp5a7Huc2qH7jtJO06o7JrQJ5fdj69MuY+5pPrVj1nFfArGtvdx3xLGQCbB7EbKPDY7Vqq+TSlNKptJ5eoNBpUIpVRNOBXPBxQWdSvA+fW6KC5E7XD1DLtPL1cu86o6JzILgprSnncfezdqqX35DcVbcDoQewGCjt22wafdW0ptzdJQyLt+pfbJqBVxQFZsxXLf08qC3WOmb2cS4pby5byWFRzJhXr/bMrtf/WE7TT9HJtM6FY+2xZoYklMbW5jcbl7tt+B4DNZ9DTVueee66OPPJIbbnlltpuu+0UCv13Oaj53e9+N5THB6C/JWTf/a502WXSvvvajr4DPk+2xHtCSUzLGjrcbt220dik8piqSiKKherUnMi6HuZ1zUmlvVxn/7VwQFMr4wr6A5peXeyq26ZXxrV1Tc+NTPrbjKyxPa23a1tdqxcbpFuCvfvPARhexG6gsGO3sUr00nhYTe0p1x99YV276lqTbtBdUx7TnrOr3MbhiUy2z6rzvuQnv2m7Bow+xG6gsGN3vtVpRXFYiaWdtec1ZVGF2/0qDlkiPOdWeK9qTau8KK3tpsS1x6wKN+6OBPyuar01ldX0ys59TGpK424Pk2Afm4IDGKVJ9C9/+ct65JFHdOCBB6qqqorNh4DNzaq4P/hB6Z//lC6+WLrwwh67gG+IJa93ml6mgE+u4rw9kdWM6rjKY2HXE/Xfb9XJy6W0zaQSebmciqIhRUIBTSgOKxoJaZfpFUplPVdtbvdlCfL+lonn9e6VTrAHNi9iN1DYsbt7wntxXYsmlUfd4Ntiq1Wlx4J+97XF4MmD2MS7v8lvACOP2A0UduzOr/byeZ1j8Gw2p52mVWhxbYtaE2nVtieVTOXcBqIWw63ly7TKIm07pUz1rUm3f1lnTO/cD6WmNKJUxnMbhbOCDCiQJPpPf/pT3Xfffa4aHcAIBHKLoiee2BnIbTZ8kDoH2HG1pToH3xVFPs2u7my9Yl+vaU5qSUObIkG/qosi2nFGuTqSOZUXBZXzfK5KPb+JSb7C3N4QdKSzmlQWdUvUeifTWS4OjCxiN1DYsbt7wlsqcZuJ1remVFkSVtjvVyQUdJuMWUxmtRcwNhC7gcKO3fnJ7/ayiKqKI3prTYuW17cpGgm4PUySOU8JX1Y+S7Tb73KXzp+1VWXW+3xWdbFLmE8si7rWbNYW1VagsYIMKJAkemVlpWvlAmAz6uiQvvpVqahIuuYa6bTTNunuLKBvWV2k1c0JF6jzPcptBjwaDmqrCSWuf3lbOqtl9QmFQn7tMK3MbT7WPWCvbOpwifdoKKA3V7eoPZlxG6L0HsCzXBwYWcRuoPBjd/eYutO0ck0sibi2LolMRumMp9kTit2m4az2AsYGYjdQ2LG7a7VXUdiNn5c2dGhNS8p9DPvl2rLMmRRXWzqjyqKI5k4s0cSS6HqL0FhBBhTYxqKXXHKJLr74YrW3tw/PEQHo6ZVXpN13l37yE2nOnCE5OxZ87WI91po7rK9qm6sqt92/rb9qRyanTE6qKopouymlmlwaVSTkX2dzsnxwX9HU4SbNp1TEXVLdBvC9f9/6NjYDMLyI3UDhx+48i6GVxRFtN7Vce25ZrR2nVaqsKOIS6GwOCowdxG6g8GO3Fas1tKX07tpWdaQyqimJuJXb06qKNaE4oq0mFuvgbSfpwztN1b5bT+gqRMsXoVkLVfvoNh8FUHiV6DfccIMWLlyoiRMnatasWetsLPr8888P5fEB45dVh992m3TOOdJWW0nPPittv/0Q3bWnVc0dWtPUocnlMa1tTWqxz5OX81RdHHatXAI+n+u7ZoP1snjYbWjWWz64F4cDrho9mc52tXoBMHoQu4HCj939YbUXMDYRu4HCj92WQJ+/rNH1MV/ZlFRrKqNkJuv2J5tUHtPO0yu0zeSyddqhUnEOjE6DznQdc8wxw3MkAHqyIPrkk9IZZ3QuJYvFhuwMWdX5qqaE6tpsOVm7fD6//O/1YJtWEVcg4NOsar9mVRUpGg50tW/pLR/c7XuTymL0ZgNGKWI3UPixu/9fyeagwFhE7AYKP3Zb+9Ql9e0qiQQVDvq0dXWRisJBZT1PU8vj2rKmyN3O2qTmx9ys2gZGL5+Xb4Y8DjQ3N6usrExNTU0qLS0d6cMB+vZ//yfV1UnHHitls4PaAXygrN/5krXtrnr8lRWNCgcC2m1mheu5VhYPqSwWIogDI4A4xTlBgdoMsRvA6ETs5pygQA1z7LZU29OL1uqpd9a6nueZbFaxcEBrmpOuLVsm62nupBL5A37XEtVasrE5ODC6Y/ege6IDGCaZjHTppdIHPiD9+Medy8qGaRBus9zWdiWZybk+5RNKOnup2nXWo43e5QAAjK7YDQAACid22+rvtmRGoWBA9W1JRUJBdaSyqm/LaFVzQh3prNsc3BLoNh7va28xAAXYzsV2Bl+wYIGqq6tVUVGx3uUl9fX1Q3l8wPiwdKl04onSf/4jXXyxdOGFncvKNkP/1Giw8w1DIpPtt20LgMJD7AbGVuwGMPYRu4GxE7ttrB0PB7XXFlVa0dTh9hFLpLMumb66KeH2IpteUaS17Wm3IpzNwYHRb0BJ9B/+8IcqKSnp+pweTcAQ+/SnpcWLpUcflfbdd9hPL/1TgbGP2A2MrdgNYOwjdgNjJ3Z3X/09oSSq6qKw22DU7/e7wrW5k0o1syquio4Me4sBBaIgeqIvXrxY3/3ud/XPf/5Tq1at0pQpU3TSSSfpwgsvVDgcHvD90K8Oo0pHh7RypbTFFtK770o2UVVZOdJHBWAEjaU4RezGmETsBtALsXtsnxOMASMUuy3VZi1drCLdEuplsaCaeiXMKVAFRsbGxqkBVaJ3FwgEtHLlStXU1PS4fu3ate66rG3IMMTeeOMN5XI53Xbbbdpqq630yiuv6LOf/aza2tp0je2eDBSaV16RPvUpKRSSnn9emjlzpI8IwBhG7AaGALEbwGZE7AYKO3b3tfq799cACsugk+j9Fa4nk8lBVYUPxhFHHOEueVtssYXefPNN3XLLLSTRUVjs389tt0nnnCNtuaX085/TPxXAZvjTQ+wGNuEfELEbwGZH7AY26R8QsRvAyCXRb7jhhq7ZtP/93/9VcXFx1/es+vzf//635s6dq83FSu5t4xWgoHzhC9Ltt0tf/KL0gx9IsdhIHxGAMYzYDQwBYjeAzYjYDQwBYjeAkUyi2yYn+RnxW2+91S0vy7MK9FmzZrnrN4e3335bN9544war0K063i7de94AIyKXk/x+6dhjpcMPlz76UZ4IAMOO2A1sAmI3gBFA7AY2AbEbwGjaWPTAAw/U7373O1VUVGzyLz///PN11VVXrfc2r7/+eo8K9+XLl+uAAw7QBz7wAVcRvz6XXHKJLr300nWuZ4MTbDa2R8D3vie9+KJ03320bgEwIhtxEbuBQSB2AxgEYjfjbowCxG4AmyF2DzqJ3pu1cnn55Zc1c+bMQSfWa2tr3Yak62P9z/O91lesWOGS5+9///v1k5/8RH6r7B1kJfr06dNJomPzWLpUOukk6bHHpG9/W7roItshiLMPYLMPxHsjdgP9IHYDGCRiN+NujDBiN4DNFLsHvbHoV77yFe2www769Kc/7Qbh+++/v5544gnF43Hdf//9Lsk9UBMmTHCXgbAKdKuk22233XTXXXdtMIFuIpGIuwCb3R/+IJ1xhmR7B/zrX9J++/EkABgxxG5gAIjdAEYRYjcwAMRuAJvRhjPRvdx7773aaaed3Od//vOftXjxYr3xxhs655xzdOGFFw7HMboEuiXnZ8yY4fqgWwX7qlWr3AUYlV59VbIJJWvjQgIdwAgjdgMDQOwGMIoQu4EBIHYD2IwGXYlu7VcmTZrkPv/rX/+q4447TnPmzNEZZ5yh66+/fjiOUQ899JDbTNQu06ZN6/G9TexGAwxtAP/Pf6TPfU664ILO/ud2AYARRuwG+kHsBjBKEbuBfhC7AYyQQVeiT5w4Ua+99ppr5fLAAw/o0EMPdde3t7crMEz9nk877TSXLO/rAow4ex3efru0++7STTdZU0DJ2g2RQAcwShC7gV6I3QBGOWI30AuxG0ChJdFPP/10feITn9C8efPk8/l0yCGHuOufeuopzZ07dziOERi9Ghqk446TPv956dRTpSeesGb8I31UANADsRvohtgNoAAQu4FuiN0ACrGdyyWXXOIS6EuXLnWtXPIbd1oV+vnnnz8cxwiMXhddJD38sHTffdJHPzrSRwMAfSJ2A90QuwEUAGI30A2xG8Ao4PPGUU+U5uZmlZWVqampSaWlpSN9OChU2ay0YIG07badM+ItLdKMGSN9VADGAOIU5wTDhNgNYJgQuzknGCbEbgCjLHYPuJ3Lhz70IXfneVdeeaUaGxt7bHyy3XbbDeaYgcKzbJl00EHSAQdIbW1SRQUJdACjFrEbIHYDKCzEboDYDWB0GnAS/cEHH1TSNkx8z+WXX676+vqurzOZjN58882hP0JgtPjDH6SddpLeeUe6916pqGikjwgA1ovYjXGP2A2gwBC7Me4RuwEUehK9d9eXcdQFBpCuvlo69lhp//2lF1/srEQHgFGO2I1xjdgNoAARuzGuEbsBjKWNRYFxJZeT/H5bVykVF0tf/KLk8430UQEAgP4QuwEAKCzEbgBjqRLd5/O5S+/rgDHJVlrcfru0zz5SIiHNmyf9z/+QQAdQUIjdGFeI3QDGAGI3xhViN4CxWIluy8pOO+00RSIR93UikdAXvvAFFb3XF7p7v3SgoDU0SJ/7nPTb30qf/3xnYAeAAkTsxrhB7AYwRhC7MW4QuwGM1ST6qaee2uPrk046aZ3bnHLKKUNzVMBIefxx6fjjpebmzs1DP/5xngsABYvYjXGB2A1gDCF2Y1wgdgMYy0n0u+66a3iPBBgNli+Xpk+X7r5bmjlzpI8GADYJsRvjArEbwBhC7Ma4QOwGMJZ7ogNj1rJl0pVXdrZtOe446d//JoEOAMBoRuwGAKCwELsBFDiS6Bjf/vhHaaedpJtuktas6bzOzz8LAABGLWI3AACFhdgNYAwgW4jxKZGQvvQl6ZhjpP33l158UZo4caSPCgAA9IfYDQBAYSF2AxhDSKJjfLrlFumOO6Sbb5Z+9zupqmqkjwgAAKwPsRsA/p+9+wCPrC7bP35Pz0x6NlvZSu+9CSgCAiKo2BAFROCPqKioIIKKqDSx4St2VIrYwPKiKCgoXZHeYWFhd9meZFMmk+kz5389v2HyZrOb3eyS3cxMvp/rCmEmM5Mzk2zuOc95zvMDqgvZDaCGUETHxGEzz596qvT/n/hEqfv8Yx+TfL7x3jIAALAuZDcAANWF7AZQoyiiY2Lo6ZFOOEHaZx9p8WIpFJJ23HG8twoAAIyE7AYAoLqQ3QBqWHC8NwDY7B54QPrgB6V4XPrNb6Q5c3jRAQCoZGQ3AADVhewGUOPoREdt+93vSguHzppVGt/y3veO9xYBAID1IbsBAKguZDeACYAiOmpTsVj6fNhh0iWXSHffTQc6AACVjOwGAKC6kN0AJhCK6Kg9t9wi7b67tGqVNGWK9IUvSEEmFwEAULHIbgAAqgvZDWCCoYiO2pFOS5/8pHT88dI221A4BwCg0pHdAABUF7IbwARFey5qwwsvSO9/vzR/vvT970sf/7jk8433VgEAgJGQ3QAAVBeyG8AERhEdtaG/v1Q0f+ih0igXAABQ2chuAACqC9kNYAJjnAuqV2+vdNFFUi4n7bef9NhjFNABAKhkZDcAANWF7AYAhyI6qtO//y3tuad09dXSc8+VrvPz6wwAQMUiuwEAqC5kNwAMouqI6lIoSJddJr3pTdJWW0lPPintscd4bxUAABgJ2Q0AQHUhuwFgLRTRUV1uu600wuXCC6V77pHmzBnvLQIAAOtDdgMAUF3IbgCo/iJ6JpPRnnvuKZ/PpyeeeGK8NwdbSvlnfeyx0tNPS5dcIgVZFxcAqgHZPUGR3QBQtcjuCYrsBoDaKaKff/75mjFjxnhvBraUdFr65CelvfYqdZ77fNIuu/D6A0AVIbsnGLIbAKoe2T3BkN0AUFtF9Ntuu03/+Mc/9K1vfWu8NwVbwvPPSwccIF1zjfT975fmoAMAqgrZPcGQ3QBQ9cjuCYbsBoBRqZp5GKtWrdKZZ56p//3f/1UsFhvvzcGWWAX8yCOl2bOl//6XxUMBoAqR3RMM2Q0AVY/snmDIbgCorU50z/P04Q9/WB/96Ee17777btQct3g8vsYHKlyxWPq8997S5z8vPfIIBXQAqEJk9wRCdgNATSC7JxCyGwCqq4h+wQUXuAVC1/fxwgsv6Oqrr1Z/f78uvPDCjXr8K664Qs3NzYMfs2bN2mzPBWN0FNzmndvCoXV10pe/LNXX89ICQAUhu7EGshsAKh7ZjTWQ3QCwSXyeHW4eJ52dnVq9evV6b7P11lvrhBNO0F/+8hdXVC8rFAoKBAI66aSTdP3114/YiW4fZdaJboX0vr4+NTU1jeEzwetSKEhf/7p08cXS/vtLv/61NHcuLyqACcdyyg76VnJOkd1wyG4AcMhu9rurBtkNAK8ru8e1iD5ar7766hqjWJYvX66jjz5av//973XAAQdo5syZNfMGZ8JZvlw6+WTp7rulL36xVEgPVs2ofgAYU7WUU2R3DSO7AWAQ2V3br0nNILsB4HXnVFVUK2fb4pJDNDQ0uM/bbLPNqAvoqOBZbF1d0j//KR122HhvDQBgjJDdNYzsBoCaRHbXMLIbACbGwqKoMel0ad55T49kB0GeeIICOgAAlYzsBgCgupDdADCmqqITfbi5c+e6lcNRhV54QTrxxNLnN7xBOuYYyc+xHACodWR3FSO7AWBCIrurGNkNAGOO6iW2DDvo8fOfS/vsYyvPSP/9b6mADgAAKhPZDQBAdSG7AWCzoYiOLeP556WPfET64AelRx6R9tiDVx4AgEpGdgMAUF3IbgDYbKpynAuqyJNPSrvuKu28s/T006XPAACgcpHdAABUF7IbADY7OtGxeRQK0uWXl8a3XHtt6ToK6AAAVC6yGwCA6kJ2A8AWQyc6xt7y5dLJJ0t33y1deKF06qm8ygAAVDKyGwCA6kJ2A8AWRREdY2vRImnffaVwWLrzTunww3mFAQCoZGQ3AADVhewGgC2OcS4Yu9PIzJw50nnnSU89RQEdAIBKRnYDAFBdyG4AGDcU0fH6zZ8v7befdOutks8nXXCB1N7OKwsAQKUiuwEAqC5kNwCMK4ro2HSeV1o0dO+9pWRSmjmTVxMAgEpGdgMAUF3IbgCoCBTRsWnicemDH5ROP1068UTp0UelPffk1QQAoFKR3QAAVBeyGwAqBguLYtMEAtIrr0i/+U2piA4AACob2Q0AQHUhuwGgYlBEx+gVi9K3viUdf7y0/fbSgw+WZqADAIDKRHYDAFBdyG4AqEgU0TE6y5dLH/qQ9K9/Sc3NpSI6BXQAACoX2Q0AQHUhuwGgYlFEx4b97W/SqadKoZB0553S4YfzqgEAUMnIbgAAqgvZDQAVjYVFsX49PdIHPiAdeKD01FMU0AEAqHRkNwAA1YXsBoCKRyc61u3FF6Xp06XWVumhhxjfAgBApSO7AQCoLmQ3AFQNOtGxJs+Trr1W2msv6bLLStftsAPzzwEAqFRkNwAA1YXsBoCqQxEd/6evT/rgB6XTT5dOPFG66CJeHQAAKhnZDQBAdSG7AaAqMc4FJYmEtPfeUleX9JvflIroAACgcpHdAABUF7IbAKoWRfSJrlgsjWppaJDOO09661ulefPGe6sAAMBIyG4AAKoL2Q0AVY9xLhPZihXSUUdJ3/te6fLHPkYBHQCASkZ2AwBQXchuAKgJFNEnqr/9Tdp9d+m556Rddx3vrQEAABtCdgMAUF3IbgCoGRTRJ5pcTvrMZ6Rjj5X231968knpiCPGe6sAAMBIyG4AAKoL2Q0ANYci+kQTCEgvvSRddZV0663S5MnjvUUAAGB9yG4AAKoL2Q0ANYeFRScCz5Ouv16aM0c67DDpL38pLSYKAAAqE9kNAEB1IbsBoKbRiV7r4nHppJOk006T/v730nUU0AEAqFxkNwAA1YXsBoCaRyd6LXvoIenEE6XVq6Vf/1r6wAfGe4sAAMD6kN0AAFQXshsAJgSK6LUqn5dOOUWaMkW6805p663He4sAAMD6kN0AAFQXshsAJoyqGufy17/+VQcccICi0ahaW1t1/PHHj/cmVZ4VK6QlS6RgULr9dum++yigAwDGDdk9CmQ3AKCCkN2jQHYDwIRTNUX0P/zhDzrllFN02mmn6cknn9QDDzy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vfOELOv/889d5TBtP22Q7a6lrY+ahrV/tdbviiivc97Xe8PZewp7L0O9lmW9jcGvRa49v2W0n8o855phh92qzMb29NxnpeFDDJnoqPDBeHnroIbfc6B//+Ie7XiqVXPuRs846a9TtXL74xS967e3tbnnUXXfd5e21117u9j/84Q/ufu9617vcdVteNRq33nqru//Pf/5zd3358uXu+t133+319fV5oVDI3cc89dRT7nNf+cpXNvq5j0c7l5122sm9FvZhS8jOPPNM9z2OPvroYR9nfe1cnn76afe1g0vObCm0fc/rrrvOXbdlat///vfdv5PJpHtdTj/99DUeY0PtXBYuXOi+bu3X78knn/TC4fAat6dSqXW+/rLLLnPtZV555ZU1Xhs77vPPP3+d+9vt0WjUe+mll1bfZm147PYrr7xy9W2nnXaaN2PGDK+jo2ONr3//+9/vNTc3rz4W+9nZ186bN2/Y4wOAWkS2b362v+c97/He/OY3u39bi7Dp06e79zvDtbJrbW319thjj1F/nw0tH7f3Oi0tLetkur3Psgwcevtw2XfDDTe4Y7znnntW33bRRRe526wd3NrsvcLa91+1apUXi8VWt9sz9n7EXp8XXnhhja+3vLf3EosWLXLXB1+jpqYm9zgAUKvI48rPY3uM4T7WbiG7OT/Ltf3rX/9y9/nlL3+5TuvWtW8fLuft2BKJhGuLu/ZzsTa4Qw2+TtYat6ura/Xt1gLWbr/55ptX32av82te85o1Htee54EHHuha6Ay65ppr3NcefPDBXqFQGPF5onZV5vRWYAzY2U5bOjR4ltLO9L7vfe9zs5jWXiI0kosuukhTpkxxS4htGZHNRLdZU3Zm0wy21rClyKNhs6xtVrnNWB6cYR2JRNySIzsLu/vuu69u6TJ4OZpNRW1zkaFnlweXStmM7qG32/VNZcuf7LWwj5133tnNwLKZZGu3IRkN+3qbVT74Ojz++OPujLi9PsYuB5//f/7zH/fz2tjNVe2svi0ls81ahr4G9rO0mel33nnn6vvaWf1Bdhx2PzsGe59gbWfWZjPWh/OWt7zFLSMbZD9P23ndZtIZe7w//OEPOvroo92/hx6XnZm3n88jjzyyxmOecsopaxwfANQysn3zs92WiVvLsBUrVuiOO+5wl8MtHR98nzPa9zijYbPLbSWYzXIb+hxsVdsBBxwwYjbbyi67n80iM2tnpfnYxz427PfcZZddXOuYQfY+Zscdd1ydzYMr0uw+Nkt96HFZrtt7ENusbShbZWaPAwC1ijyu/Dy2lmaWu2t/2Orssf5ZDs1T27jUVn8NfW1slrnVO0bK+b6+Pnc/y2JbHWa1h6GsTYvNaB+OHavl96DBzB/M+a6uLvf6Wl1g8PvYh81utzG4zbq3bgFD2Yo2e28CrC28zi1AFbA/9vZH34JgsH+5sQGaLd2xlh6HH374Bh/Hluna8mArfFvLEuvrZX/AB1mB1Ngf49G0NBl8jKGF8r322mt1gAwtHtulLaeyHp4bYm1BrrvuunVut+VJQ9lSZQvqTWEh/OMf/9iFqvUQs0K0tTTZFPYY9lxtUGqFbnuu9liDO4Db5773ve9t9MmEoSwMrVBtxzkcO3kxaNGiRW6pli2TW7tX29pvhmw39ZH6ollP9bVZoA8+pvWOt+KB9Wyzj+GsWrVqjetz584d8TkCQC0h28cm2wf39PjNb36jxx57zJ3It/wd2kpl6Psce48zViybh+4xM9z3G2SD3i9+8Yvu/dza2ThcoWKkvNxQNg8el/VgH6kwTjYDwP+Qx/7IY2tXYieDt8TPcmieWkaPVCcYmqfWYsX6uFuBe+19v9bOedsfbKS2pmvn/GBBfTDnra2b1QWsfZx9jHRc9j0GMQbHSCiioyrZH+Lly5e7QLCPtdnZ1tGEgRVg1xc8g5t6PPnkk2vMclofKwb/8Ic/dMXUwT7gg+zfNrPb+nfZLG07Y2sF6w0599xzddJJJ63Rd9uuW8/uPfbYY/XtQ8/QbqzRhPDGsNfh5ptvdq/dcK+D9VGzM8L2OthmI7YR2Maw4rwV623DluHOItuZ8ME3Dnam3Abr5513nvuZ2nO17209U4duKGbsJMpIPepHOls92Lt18LHsZ2MzzIdjs9eHYhY6AJSR7WOT7ZZjtqLOTr7bLC3bmHMklok2sM/lcmOyJ8dgDlpfdFsZtjY7UT3IZozZ3jD2fsB6oFpu29e/9a1vXSeb15eXG8rmweOy9wL2fmo4O+yww6i+FwDUAvLY/3k81j/LoXlqBfSRNiYdPFlttRA76WAnB770pS+51dxW97CVZjYmXzvn15e7ox2D2x5jNvN8OIOT+Ubz/VDbKKKjKtkfbfvj/f3vf3/YNh+2eYQVsjf3j6O15bjsssvcRlQbU0S/6qqr3MaS1irEBodDi8fpdNptJGlBOnTTyvWxpcr2MWjw7LUV4a0NTSUanFluRXIrog9dVmbHbW8q7Ey+bT5qZ+k3lgWxBaedRV578DuUFfFfeOEF9+Zl6EaittRtrNmbBpttYIX7sTwhAQC1gGwfu2y35eJ20t5OCtuGnet7n2Nt1awVmbVg2VyDLc/sPdr6ctBmj9nsN5uJbivF1p7JPtbsuKw1HtkMABtGHvs/j8frZ2l5anUO21R0fV9j43xrp2Lf4w1veMPq24fOhh8rg5PxbCU6OY/NRU90VB0rQtsf46OOOsrtXr32h7U+saVQ1rpjc73uda9zM6J+8pOf6MYbb1zn83am2M54Dlc8/uY3v+lmnA+dgW0tU2bMmKGvfe1ra9y3Gu27777ubLMFt836Hvo6WAHddiW3MLce5ZvyOthZfTsrbQPwobPNjF230B565nrofezftkP4WLPvZSdG7M3PU089tc7nrd0LAGBdZPvYsmXbl1xyiWudNtyM8KF9xu19yTnnnONOOA+3/PnLX/7yqL+vzQCzWWeXXnqpew80Ug4Ol83m29/+tsaDzXq34sTf//73dT5ns+UKhcK4fF8A8BvyuDryeLx+lpanNmHMntPaLEstU0fKeaud/OAHP9BYs5MEdrLj6quvdrPu18YYHBuDmeioOvZH3v7Yv+Md7xj287Yplc0ItuKtbUKxua6//nq3xMmKtnaG+M1vfrNrB2KzpWxJlP2htqVeQ3t2zZ492w3WrGhurUqGsmKyFVmtFYmdwa1WtgzN+r7961//ckVzO5O/9utgfdjMSEV062823JsF6zNvm57a5y644AI3M9961tkscDu7bWfUrd+9neCwpXF2xtz+bcV8G9zb6792b/Sx8tWvftVtqGJ95mzDEltBYK1kbOmanbW3fwMA1kS2jy2b8WZ9SDfElqZbZtqKMGupYsvXB/PacuuGG25wEwrWHowOl822MuzEE090q/FOPvlkd7LcZt3ZezLbm8RW4dn7HiskWBbbzDSbVGDFdutTetttt43LDDVjqwLtd8wKCdbKzZ6jncS31Wq///3v3fuIyZMnj8v3BgA/IY/9k8fWV9xWzA/Hvn5Tf5YPPfTQsDlvhWpr0fLRj37Urda39jNWJ7EZ4FYbsU1HbaKaFehtrG/PyVqcnnnmma72Ya3e1j55PlZscp7VFF7zmte4MbjNTrcWuFaTWbJkiR5//PFx+b6oPhTRUXXsj7zNcLbeliMFlRVY7X42G3nSpEmb9f0sWKxnp501tU1BPve5z7mzqHPmzHGBdNZZZ63zNfYH3IJu6OzrQTaAtCKuFXc399gqnb0OVkQfbN+y9utgRXQrfA/tNTfU888/P+zmIKeddpr7GZ9//vmulcu3vvUtNyPd2AkMC/PBNwsW6tab3cLbwt5+d971rne5M+8jfd/NYTufP/DAA673m535t98b+znbhrOXX375mH8/AKgGZPvEsZO+tnrqiiuucIVuG+Tae6mdd97Z5azl5dqz4YbLZptkYEV0W7puEwjspLI9ZjabdUVya4t36qmnrr7/r371K33yk590A18bVFt22z4na08+GAuJREJ33323myFvg3ybIGGFfHsPYe8fmpubx/x7AoAfkcf+yWMrDttJ65GK6Jv6s7R2q/axNpt9Prj/m43vbeb3Zz/7WbffiU0etO85OEnQHuuWW25xM+vtJIIV1O3z9l5hpL7lm8Mmrlnx3zL92muvdc/HZqjb5LuhbeOADQl443WqBwAAAAAAAAAAn6MnOgAAAAAAAAAAI6CIDgAAAAAAAADACCiiAwAAAAAAAADg9yJ6sVh0mxTNnTtXdXV12nbbbd3GBbR0BwCgMpHdAAD4C9kNAMDwwvKJyy+/XFdddZWuu+467brrrm5n3VNPPVXNzc0688wzJ/rwAADAWshuAAD8hewGAGB4Ac8nU7mPOuooTZs2TT/96U9X33bssce6Wem/+MUvJvTYAADAushuAAD8hewGAMDn7VwOPPBA/fOf/9QLL7zgrj/++OO699579ba3vW2iDw0AAAyD7AYAwF/IbgAAfN7O5fzzz1cymdROO+2kUCjkerV95Stf0Yknnjji12SzWfcxqFQqqaurS5MmTVIgENhCRw4AwOjY4rC+vj7NnDlTwaBvznOPiOwGAFQ7sptxNwCgRrLb84kbbrjBmzVrlrt84oknvOuvv95ra2vzrr322hG/5qKLLrJWNXzwGvA7wO8AvwP8Dvjqd2Dx4sVeNSC7J/53iQ9eA34H+B3gd4DsJrv5O8DfAX4H+B3gd4DfAW32uNs3PdFnz57tZrSdccYZq2/78pe/7PqhP/fcc6Oaid7b26utt95aixcvVlNT0xY5bgAARmQRfNhhUleX9LOfKTlvnsu7np4et3G235HdAICqQ3avg3E3AKAWsts37VxSqdQ6U+ytrYu1aBlJLBZzH2uzAjpFdADAhEkmpc5Oae5c6be/lSZPlhoayrfbrt9V0nKM7AYAVA2ye8SXhnE3AKAWsts3RfSjjz7a9UC3meS77rqrHn30UX3zm9/Uhz70oYk+NAAARu+BB6Tjj5dmzJD+9S9pm22q9tUjuwEAVYHsZtwNAFCtZ7dviuhXXnmlLrzwQv2///f/tGrVKtf8/aMf/ai+8IUvTPShAQCwYbZy6utflz73OWnvvaXrr7dT31X9ypHdAABfI7sZdwMA/KU0fuNu3/REHwvJZNL1urHe6LRzAQBsUe97X3kJ2XnnSZdcIkUi69yFnFoXrwkAYMKQ3ZuE7AYAVGN2+2YmOgAAvj0Tbnt6nHyy9OEPlzc0AQAAlYvsBgDAX0rjP+6miA4AwHjIZqULLpCWLZNuuEE66iheZwAAKhnZDQCAv2S33Lg7OG6PDABArXrhBenAA6XvfU864ICJPhoAALAhZDcAAP7ywpYdd1NEBwBgLNnGJbaBSV+fdP/90tlnV/0GogAA+BrZDQCAv1y/5cfdFNEBABhLL74oHXec9Mgj5VAHAACVjewGAMBfXtzy4256ogMAsLkeeEB6/HHp9NOlL36xvKEJAACoXGQ3AAD+8sDEjrsZ5QMAsDk7gH/ta9JBB0nXXScVixTQAQCoZGQ3AAD+UqqMcTdFdAAANsWKFdJb3yqdd550zjnSnXdKoRCvJQAAlYrsBgDAX1ZUzribdi4AAGwKC/Enn5Ruu0067DBeQwAAKh3ZDQCAv5xXOeNuiugAAIxWLlfewGTXXaVvfKO8rGzqVF4/AAAqFdkNAIC/5Cpz3E0RHQCA0bAQP/54aeVK6eWXpcmTed0AAKhkZDcAAP7yYuWOu+mJDgDAhlx/vbTXXlIyKd10kxSN8poBAFDJyG4AAPzl+soed1NEBwBgfT73OemUU6T3vEd65BFp7715vQAAqGRkNwAA/vK5yh93084FAIDhFIvlXb/f/W5pl12kE0/kdQIAoJKR3QAA+EvRP+NuZqIDADCUbVpyxRXSG95Q3tBkn30qOsgBAKh5ZDcAAP5S8t+4myI6AACDVqyQ3vpW6dxzpYMPlgIBXhsAACoZ2Q0AgL+s8Oe4m3YuAACY226TTj65HOB//7t0+OG8LgAAVDKyGwAAf7nNv+NuZqIDAGBWrizvBP74474KcgAAahbZDQCAv6z077ibIjoAoHa9+KJ02WXlf9vZ8L/+VZo2baKPCgAAjITsBgDAX16sjnE3RXQAQG36+c+lvfeWfvYzqbu7fJtPerEBAFCTyG4AAPzl59Uz7qaIDgCoLX195bPfH/iAdOyx0iOPSK2tE31UAABgJGQ3AAD+0ld94242FgUA1JbvfEe68UbpF7+QTjxxoo8GAABsCNkNAIC/fKf6xt3MRAcAVL9SSXr00fK/P/OZ8iYmVRLkAABUJbIbAAB/KVX3uJsiOgCg+nf/fvvbpQMPLP87FpPmzZvoowIAACMhuwEA8JeV1T/upogOAKhef/+7tPvu0mOPSTfd5MsdwAEAqClkNwAA/vL32hh3U0QHAFSnn/5Ueutbpb32Ki8jO/zwiT4iAACwPmQ3AAD+8tPaGXdTRAcAVJdisXxpQW6bmfzlL1V7JhwAgKpAdgMA4C/F2ht3U0QHAFQP2/n7Na+ROjulrbaSzjxTChJ1AABULLIbAAB/+UVtjrt99QyXLl2qk046SZMmTVJdXZ1e85rX6KGHHprowwIATLS+PukDH5BOPlnad18pGp3oI8KryG4AwLDI7opFdgMAhtVX2+PusHyiu7tbBx10kA499FD99a9/1ZQpU/Tiiy+qtbV1og8NADCRHnlEet/7pBUrpOuvLwc6KgLZDQAYFtldschuAMCwHmHc7Zsi+uWXX67Zs2frmmuuWX3b3LlzJ/SYAAAVIJ2WJk+W/vpXabvtJvpoMATZDQAYFtldschuAMCw0oy7fdPO5c9//rP23XdfHXfccZo6dar22msv/fjHP17v12SzWSWTyTU+AABVYOVK6YILypuZHHSQdN99FNArENkNAFiN7PYFshsAsBrZ7c8i+vz583XVVVdp++2319///nd9/OMf15lnnqnrrrtuxK+57LLL1NzcvPrDZrIDAHzuttukPfaQbGXS/Pnl2wKBiT4qDIPsBgA4ZLdvkN0AAIfsXkfA8zxPPhCNRt1M9PtstuGrrIj+4IMP6j//+c+IM9HtY5DNRLdCem9vr5qamrbIcQMAxkguJ33+89IVV0hHHCHZSdRp06rq5bWcspO+1ZJTZDcA1Diy23fIbgCocWS3/2eiz5gxQ7vssssat+28885atGjRiF8Ti8VcEWLoBwDAp/70J+nb35a+/nXpL3+pugJ6NSK7AaDGkd2+Q3YDQI0ju/2/sehBBx2k559/fo3bXnjhBc2ZM2fCjgkAsAU89JC0777Se98r7bWXtMMOvOw+QXYDQI0iu32L7AaAGkV2V89M9LPPPlv333+/Lr30Ur300kv61a9+pR/96Ec644wzJvrQAADjoa9POuUUab/9pIcfLvc9p4DuK2Q3ANQYstv3yG4AqDFkd/UV0ffbbz/96U9/0g033KDddttNl1xyib797W/rxBNPnOhDAwCMNSua77OP9Mc/StdfX/43fIfsBoAaQnZXBbIbAGoI2V2dG4uOhWrbsA0AqtI//iEdeaS0++7SDTdI22+vWkFO8ZoAgC+R3Ywxh+D9DAD4ANmtja0P+2YmOgCgyhUK5cuDDpIuuUS6776aKqADAOA7ZDcAAP5Cdm8yiugAgIl3223SjjtKzz0nJRLSeedJ0ehEHxUAABgJ2Q0AgL+Q3ZuFIjoAYOLkctK550pHHCFtu63U0sJPAwCASkZ2AwDgL2T3mAiPzcMAALCRFiyQ3vc+6dFHpa99TTrnHCnIuV0AACoW2Q0AgL+Q3WOGIjoAYOIUi+Xe5/vtx08BAAA/ILsBAPAXsntMMOUPALDl9PeX27ckk9LcudJDD1FABwCgkpHdAAD4C9k9LiiiAwC2jEcekfbeW/rBD6THHivfFgjw6gMAUKnIbgAA/IXsHjcU0QEA46tUkr71Lem1r5UaG8s90N/wBl51AAAqFdkNAIC/kN3jjiI6AGD8z4R/+tPSmWdK//mPtP32vOIAAFQyshsAAH8hu8cdG4sCAMbHgw9K++wj7buv9NxzFM8BAKh0ZDcAAP5Cdm8xzEQHAIytfF46/3zpgAOk3/2ufBuzzwEAqFxkNwAA/kJ2b3HMRAcAjJ3586Xjjy8vJbv8cum443h1AQCoZGQ3AAD+QnZPCIroAICx8eyz5dnnU6ZI990n7bcfrywAAJWM7AYAwF/I7glDOxcAwOYpFMqXO+4oXXih9OijFNABAKhkZDcAAP5Cdk84iugAgE1nbVt22026/XYpGJQ+8xmpqYlXFACASkV2AwDgL2R3RaCIDgDYeKWS9K1vSa99rVRfL229Na8iAACVjOwGAMBfyO6KQhEdALBxOjulo46S/u//pE9+stz/fIcdeBUBAKhUZDcAAP5CdlccNhYFAGycSETq7pb+8hfpbW/j1QOAGuR5nnpSeaXzBdVFwmpJRBQIBCb6sDASshsAAH8huysORXQAwIbl89Ill0innirNnVuefU6xBABqtni+IpnWit6M6iIhhUJBbTelQa310Yk+PAxFdgMA4C9kd0WjiA4AWL/586Xjjy9vZrLzzuUiOgV0AKhJVkB/qb1fq3rT6hjIaY9ZLcoWSm5GeqsoolcMshsAAH8huysePdEBACO74QZpzz2ljg7p3/8uF9MBADXLiuWlkqeZrQlZ85ZlvWkFgwHX0gUVguwGAMBfyG5foIgOABje8uXShz8sHX209Oij0v7780oBQI2zYrkVzbP5oma1JTR3Ur1r5WI90VEByG4AAPyF7PYNpowAANb0xBPSdttJM2aU/z1vHu1bAACOFcutaM6GohWG7AYAwF/Ibt9hJjoAoMzzpG9/W9pvP+mb3yzftu22FNABAKsFAgG3gejMloS7tOuYQGQ3AAD+Qnb7FjPRAQBSe7t06qnSrbdKZ58tfeYzvCoAAFQyshsAAH8hu32NIjoA1LrubmmPPaRCoVxEf/vbJ/qIAADA+pDdAAD4C9ntexTRAaBWWdE8FJJaW6UvfEF65zvLfdABANhMnuepJ5Wnd/pYI7sBAPAXsrtq+LYn+le/+lXXg/FTn/rURB8KAPjPggXSwQdLV19dvv6xj1FAx7gjuwF/F8W7B3Ja1pNyl3Z9fayA/lJ7vxZ3pd2lXcdmIrsxAchuANgMZHdV8WUR/cEHH9TVV1+t3XfffaIPBQD859e/lvbcU1q1qnwJbAFkN+BvG1sUT+cLKpU8TWmMuUu7js1AdmMCkN0AsBnI7qrjuyJ6f3+/TjzxRP34xz9Wq7UgAACMTjotfehD0vHHl/ueP/qo9NrX8uph3JHdgL/ZrPMVybRW9aYVDQdVLJY2WBSvi4QVDAbU3pd1l3Ydm4DsxgQhuwFgE5HdVct3RfQzzjhDRx55pN7ylrdM9KEAgL9EItKKFdI110i/+pXU3DzRR4QaQXYD/mazzlf0ZtQxkNOTS3qUyhc3WBRvSUS03ZQGzW6rc5d2HZuA7MYEIbsBYBOR3VXLV1NCfv3rX+uRRx5xy8pGI5vNuo9ByWRyHI8OACqQ9az97nelffeVDjpIuvVWKRCY6KNCDSG7Af+zWed1kZD2mNWiZb1pzWiKr1MUH24j0db6qFoVnbDj9i2yGxOM7AaAjUR21wTfzERfvHixzjrrLP3yl79UPB4f1ddcdtllam5uXv0xe/bscT9OAKgY7e3S0UdLtgHzPfeUb6OAji2I7AaqgxXFQ6GgsoWSpjTGNb25ToG18mSknukbuyFpzSO7McHIbgDYSGR3zQh4Pnkne+ONN+pd73qXQqHQ6tuKxaJ7Ax8MBt2M86GfG2kmuhXSe3t71dTUtEWPHwC2qDvukE46SSoUpGuvLfdAR8WznLKTvtWSU2Q3UB2Gm2W+dhHdiuRWQLeNRK0PurVxmdmScIVzK6rb5qLWG91au9gMdQyD7PYlsptxN4AaRnbXVHb7pp3Lm9/8Zj355JNr3Hbqqadqp5120nnnnbdOAd3EYjH3AQA1xU4efvCD0i67SD//uTRjxkQfEWoU2Q1UV/F8RnOdu324gvpIG4na/ayAPlhct+u0eBkG2Y0KQXYDwCiR3TXHN0X0xsZG7bbbbmvcVl9fr0mTJq1zOwDUpAULJGt3ZUVza9+y9dZS0Dddu1CFyG7A3wZbtAydRW6Gm1k+uJHo0OK6GSyur0pmlM4X1ZvOqy6SG3Y2e00iu1FhyG4A2ACyu2ZRXQGAavCb30h77ildcEH5+jbbUEAHAGyWobPI7dKuD3ebsYK4FdOthYtdDhbIB4vrzXURWQ/J3lcL84M902sa2Q0AgL+Q3TXNNzPRh3PXXXdN9CEAwMQaGJDOPFP62c+k979f+s53+ImgopHdgH/EwyEN5ArqWJZRXSzsrpf3I1q3bctIBovrVmxPZgq0dTFkN3yG7AZQ88hu+L2IDgA1zTYNPegg6cUXpWuukU45xaoVE31UAIAq4cnTQKag3nROJa98vTURHbZty4Z6qmfzJRdRoy2+Vy2yGwAAfyG78aoaffcKAD7meVKxKIXD5fYt1sZlxx0n+qgAAFVmVTKrrlROTfGIu7Trkxribmb5aDYHXaOneiCgKQ0xxSLBDRbfqxLZDQCAv5DdWAs90QHAT9rbpaOPls49t3z9fe+jgA4AGB8B97/Bf/7vyiit0T/d81wBfe2e6TWB7AYAwF/IbgyDIjqAMWfLt7sHclrWk3KXdh1j4I47pD32kP77X+lNb+IlBQCMq2mNcc1qSygaCrhLu74xuW8zzm0G+oL2fnUPZF1Ll5p7T0B2AwDgL2Q3RkARHcCYG1y+vbgr7S7tOjZDqSR99rPSW94i7byz9Pjj0lFH8ZICAMaVzRjfa3ar9prT6i7t+nCscP7Y4m49trjHXdp1Yy1bJjdElS0UlcmX9OzyXi3sGKiNQjrZDQCAv5Dd2ACK6ADG3BrLt0ueu47NEAxKHR3SV74i3XabNHMmLycAYNxZyxUrnG+oBcvKZEaLulJuprld2vXBr7cWLtFwyG0mujKZ0/MrkrVxcp3sBgDAX8hubAAbiwIYc275djCg9r6su7TrI7HZaDaYtkL74EZjNdUndX1+85v/9T2/+mqrRkz0EQEAathg25aVfRl5JU/xSMgVyZOZfHnzrVfbpnsq3y+Vy6u9L6cVvSn1povaqrVOkVDQZf5oNib1JbIbAAB/IbsxSsxEBzDmrBC+3ZQGzW6rc5d2fSS0fhnGwID04Q9L73+/9I9/lG+jgA4AmGCW2Y8t6dGDC7p0z4sduu2ZlXpwQbcWdQ642eaRoFzv9Hg45Nq5Pbu8T08u7VUgEHQF+IAnNdVF3Unzqts/hewGAMBfyG5sJGaiAxi35d8jzTIbOvu8N5VXsVjS1Ka4m7le1bPTRuOxx8rF88WLpZ/8RPrQhyb6iAAAcCyj09mCGuMRFT1PK3rTiocDGsgVFQuHNG9Kvbaf2ri6rVtDLKxC0dNOMxqUaoqrrT6ibSaXT64PnkS3+9mqNTvpPlLP9YpHdgMA4C9kNzYBRXQAG22wCG7LtHMFT9FwQIloZNStWOxrX1zVp2Q6r+5Uzs1YM6FQcL2tX6qezcL75CeleFx66KHyJqIAAIxL5Gx8OzW7X10srM5X+5+HgkF1DuRdC1HbOPTlVX2a2hhXc13EFcbtRHk4HFAqW1RTIuoK6IOF8qH7p/j6JDrZDQCAv5Dd2EQ1XK0CsKkGZ48lUzktS2Y0oymu5kR01LPIbKBsBfRUrqi+dEGBOrkB9/TmuvW2fqlatmloe3u5aG792NrayoV0AADGyWhmgq9daG+uC2vPWS2a0Rx3PdGzhZKeWd6rFb1ZBQMBdacKem5FUgfMneQez062bzulYY2T7Zuyf0pFIrsBAPAXshubyWfvVgFMlOFasNTHwyp0lZdr2yB8tLPIbKCcL5bU2Z/TpMaYGqIhNSci/l3GvTnuvFM66SRpm22ke++VZs6c6CMCANQAy2zL8lgkpGXdKTXEQuvMRl+70D5vUkLJTEGZXEHNdVFNbYopVygpnSsok/c069WNQzOFoma2JFyuD33/oJSdNA+rN11wBfbJ9dFhC+wVj+wGAMBfyG6MAYroAEZl6EB6IFdwS6CS6YIGsnmt7M1oZmti1LPIbKC80/QmBZR0g23bZMxautjGYhuzrNzXCgXpi1+UvvIV6Y1vlH7xCzYPBQBsMZa1qXxRL6zsk23pmejNaHpT3eoT2t6rPc/b+zKa2VynTL6ol1b164X2fhUKnnKloqY3xFSyfbmyRUXC1pLNMj2yxvuBtQvxVjjvGMj5sxc62Q0AgL+Q3RhDFNEBjMrQ3qVe0pOVt21APas1oUg4oMkN0fXOIlt7SficSQm1JKKrr9vnq2aDsdE47jjp5pvLRfRzz7WG8BN9RACAGmKZPb0xpo6+rMtbm5Vus8MHs9cye3ky4z5vLVe2bksoGPDUl85relNcz69Iqasvq7ytRMsW1Fofc63dbGPRoe8HBme8x23Ge29a6Wxe0XDInxuKk90AAPgL2Y0xRBEdwKgM7V1qG4Dasm8FAqs3BItFguudOT5S79XBgfOynlR1bDC2IbmcFI1Kn/hEuXj+utdN9BEBAGqQZXZdNKxssaQlXWm3Aei2BZuTXmY5nIiE9JpZLa7dy/TmuDK5orpSOa1MZtSTyimVLbiZ6Fu11ikUDCgaCqxzAtzeP6TzRT2/ss+dgC8komqMy1+90MluAAD8hezGOPDBu1YAlcBmlVnhe+jM8f7swAYHwYMz0Bd29Lle6nMn16ujP7dOkdz3G4xtyMCA9KlPScuXl2egv/nNE31EAIAaZ/3IbXNw29ukP1tw1wdZDttJc+t5PrW5zrV66c/kNLetXv3ZnHKFgmKhsPpznvozBU1piKklERv2/cO0pphWJTPlArvnueu2Gm2wfVvFIrsBAPAXshvjqMqqVADGc8ba0JnjVhzfLhBYo4f5+magJ19dFm5suffaPdBto7GhRfqKHlRvrMcfl97/fmnRIum7353oowEAYI0T2J39WdXFwmucwF775Lldt9nnNvO8e6CgdK6oaQ1xKZBXPBzUvMkNCgc9dfVn3dfb5qKDX2eXuZKnpd0ZN+N9+2lNbuPRikZ2AwDgL2Q3/FJEt4Jae3u7pk6dOlYPCcBnRfXhNgYd7KW+zeR6d7+2+oi2mdwwYg/0qmvh8oMfSGefLe28s/TQQ+VLoEKQ3QDc3POANJApaEUy7bJ7MMPXzuVISAq6+3vKFy3HBxQOBRVriuuVzgH3PqAzlVU0FNJ2UxtW90h3M95tTxUF1J3OKZ0ruL8/FbuBONmNCkZ2A8AwyG5sAe598GgkEglXJB905JFHarm1JXjVqlWrNGPGjLE/QgATarA4bj3L7dKuD/f555Yn9ejibi3qTLniuM1AHzrLzVq4NCWi2mZyoxuU2wy1wR7odmnF9qrU2yt97GPS/fdTQMcWR3YDWB/L4kQ0rK1aEq7X+YL2gTUyfG25gqdlPWm1J7OuAF4oFdXaEHUz0C3/n7H3Aq/06MEFnVrYkdKy7rRr52ZfZ+8FbGNR66u+oi874veoCGQ3JhDZDQCbgOxGJc1Ez2QyaxTP7rnnHqXT6TXus3ZxDYD/jbQh6Nqfb+/LqKMv6zYgs/6pgz3Phy4HtxYu9nfCCvLZfEnBQJX2QL/rLunRR8sz0M8/323ACkwEshvAcPuUDK4as1weLG5bUs1sTayR4ev8TSkUFQ4G1JiIaKAno/pYRE3xsAayReWKReVz0pTGuPv6V7r6lSkW1RgPK1+S6mNhTa6Puu+RzRcrbwNxshsVguwGgFEiu1GpM9FHo2KXZALYZIPtWNaeMT44A91mmCVTObcxmZ1He3FFUt0DWVckH1yqbUV3631q/365Y8DNVpvfYYX5kht8bzu5flQ90Dc0K37CFQrShRdKb3qTdOutUrFIAR0Vj+wGasfgie/FXWl3aexE99xJ9ZrVVi5ur+/EdkABtTXEXIuWuZMS2m1mk6Y3xjRnUp32nzdZs1rjCocCro3L1Ma4tm5JuHZuFtdN8YjboNSK9LZh6dDvMaH5TnbDh8huADWN7MYEqaKpnwDGw2A7lrVnjA8OxHtf3TDUBrxtr7Zpsf6o7f1ZtSSia8xaHyzIxyIhPb8iqbpo2BXR45HQGvcbbrZceUOz9c+Kn1ALF0onnCA98IB0ySXlGeih0EQfFQAA6+Tw5IaoFnQM6JVO27OkUTtOb9T05ro1Mncwh1O5vGvHYn3NY+GgZrfVKVMoaXpTzOX9ku6U2qIRTWuKaoepDVrem3ZF88Z4RAPZvBZ2DKipLqppTXFNH2FD8gnLd7IbAOATw42Px/OE2pb+fqNGdsMPRXT7j2XofzBrXwdQnYa2Yxk66LXrxWLJFc47+7MKh6S4F1BPuqBoOO4+Z/dp8crFb/v3YAuXZd0ppfMlRcKelvdm5Sm5TsF9uAH10FnxVtSvqKXgX/yitGyZ9bqSDjxwoo8GcMhuAMOdGLcCup0At3fyBa9/2M29bUa45bCtNluWzLgVZ011EW0zqV6ZfNG1gLET4rmip4ZYRAO5klrqY5ranHBfs6g7pcZYRIFgyRXt7fHd6rRhcnvC8p3sRgUiuwEMZ0ufcB7p+014cZ3shh+K6PYfyg477LD6P47+/n7ttddeCgbLHWEqrq0CgDEx2I7FBrNDA9MK4gO5gl5YmZb917+iN6eVyYx6M3ktaE9rh2kNaqmPuvvZpqLFUvn+DdGwJjfGlMoX1JcpanJDTJFQcPWAefB7LGhPamlXRtOaYu5+NhMuEY2sMSveernaIH/CAjyVkp55Rtp3X+lb3yrf1tKy5b4/sAFkN4DhToxbKzZLS2u1YhltGWt/L1b2ZWShbrPGBwvb9fGwCl1WKA+767bSbFFXSvNX9WtlMqVAIKiOZFY7z2xUfSSoxb0ZeSVPyXTefS8bK9gs9vUNuEda9TYuyG5UOLIbwHCF6o094TzcijIbT492zDzS95uQ1WNkNyrEqN+hXnPNNeN7JAAmxIbOJK9dOLfBdsnz3IzywYL4zOY6PbGkW5487T2nTU8t7lEyk3etXhamBhQJBhSJhPT0kh41uiXdMU1rqlMiklO+6CmbL2hVMuuC2B5/eW9Knf05Pbeyzw3a2xIxbeN6tZaUzuZdO5h5k+rd8U1Ye5cnnpDe/36pr096+WWK56hIZDeA4fK8JRFzm31a5lrkt/fl9OKqTpe91mJtzqSEm3Fu2ZpM5RUOB9SfLbjrHcmMnl6W1KLuAfWl8+79wNSmuPoyCS3sLPdat5nqgWBAK5NZzWytc4+/orfXnTS31i7bTV0zr0da9TbmyG74ANkNYLhC9XAnnNc3lv9f+9WcW30201aUJaKjHjOPdIJ7i68eI7vhxyL63LlzdeCBByocpo06UE02dCZ56Oe7BrKKhoKaO6XBBaYt6w6GgsoWSm5Ansl7ak9m1RAPuw3FbFBtg+4eWwq+PKkVfVkN5G1z0qLbxGyr1nqt6E0rkyvqvws6FbWQHsipPhpyM+RSuYISkZC6U1k9sbTHLRUvFD03mLfHj0WCW375t626+cEPpHPOkXbcUbrtNilaIS1lgLWQ3QCGzftAQFMaYi5HLYMffqVbz61Ilm9vjGlpd0pTG6OaZINxr6hMIapi0QbNQXX1Z/TCyqQWdqRUKBW1dVtCM5tiWtbd72afZwq2AWlJU5vqtFVL1K04e25ZrxZ0pVy+N2cKmtkSW+O9xtBVb+OC7IaPkN0AhitUz2iu07aT61evGiuVSlrQ3q/nV/a9epI64jb+HszXwcewSWmFQnllmbVcXZFMry66N9eF1ZsurHMSe3AG++T66Boz2Lfo6jGyGxVo1L/thx56qJYvX66pU6eO7xEB2KI2dCZ56Of7MnnlikXNX9WnnnRes1rrXLB2DOTUFAuqeXqDGmIhhYIhZQtF93iN8fIZcivAJ6Jhdfan1VLXpHAoIM+z4ntUy/NpvbSyz32+J53VnLYGZYtFlYqey04L8QfmtysejWrfbdq0qi+rnlTWbYa2xZZ/D/rMZ6RvfEP65Celr31NisfH/3sCm4jsBjBocC8T2xz0xVX9SuWimt1ar8XdA25zUJu4tqAjpY7+rHaY1qgnlyRlXRttVdlL7X2aXB9XvuRp/sqkVvRmbHTrBu2ZfEkvt6fcxuKWz3Z/21S0v1BSuiBXqLe+6XbC3VadtdXnXRsZK5xvsVZsZDd8hOwGMFyhenC/hP5s0Y3PbXZ5V39WyYwVwEPqSuXUGPtfrg4+huWyTUIbyBTcKrF0b1HJdHl1mY3lrU2bnQTPF0vaaXqTmusierljYMRJdlts9RjZDb/3RAdQfcqBLM1v73fBObUx5v57HxzQDg1wO7sdCUb1xJIeLe9Na2F7v00fUzpXVGtD1H3+9dtNcYPjNXqnZ4sqlqwYnlOhVJ6JHgzYlwa1pLtPTy/pVXc6794AFEolV6y3peQW4Bb6kXBQnX0lrepPufYvcybVu5nvWyzATS5XnnF+6qnSG94gveMd4/e9gDFCdgMYZDlp+fvY4h5X7F7aHdazK/pULHhuk1BrzTatyWaOR93s8kcWdak/U1R/Nq9kuqhENKQVPbb3SUFFN7CWEpGwu/+kRFiBUFSZfMGtIpvWHFU0ENDSrj7Nbk0oHPJULHpqTYTdQH9Z94Arqo97KzayGz5EdgMYaZw7dIJbx7KMSi7fQ3phVZ9a4mHVhUO2HEzTm+rcLHN7jIFszq0KK3kl5QvWgFWrJ9DZxDQroKdyRdfSLaCk5kyuX+8ku3FfPUZ2o4KVdwUdpS26Yd9aLrvsMu23335qbGx0s+GPOeYYPf/88xN2PEC1sEC2Jd2FYsm1arH+qFYAH/p5WzbWVBdWQzSkjr6MFncNuDPgr3Sn9Niibs3vsOXhUl/aWrdkVwfrzJaEW/5lm4q21UfUWhfRPlu3anL9q5uT9aXV2ZdRulDU5ERUkVBAO89o0o7TG7XTjCbtvXWrpjTGlQgHXWF+99ktmt4c1bzJCUVC5Rnqdnz2fez7jcvfqEJBuvBC6YADpHRa2nVXCujwFbIbqN5Cm22uvawn5S43VHhriodUKBa1tGdAAdvbREUt60qrL1ve/LujP6NJCdvgW7rv5XYt7kq7ti4rklnli0UtbE9rIFfUlIaoWznWGI9qektcu81o1rSWhBt8d6cKboC+vKc8M24g62lVX0aNdeXWMZFwyBXy7X42jE+mypuajjmyGz5HdgO1beh4eug4d+gEuFS+qHgo6E5q2zh7enNC89v79MTiHr24qs+1abGvrY9F3UqyfFEayBddDg/OcLeJablC8dUVaZ5bXWabg2/x1d6G7IYPbNR/DR/84AcVi8XWe58//vGPGg933323zjjjDFdILxQK+uxnP6vDDz9czzzzjOrryxsMAth4Fsg2sG2tjw17tnkwsG3p9qpkxs1g6x7Iug3JbJNPC2abZW79UW2GeFM84gbzg2fNbSa6DcDta63tixeQ2upjemRRj9t4rDkRUW8672ad18dCao7b8jO5Xqt2vT4WVjgYV6bguc3OJtVHXR/2pT0W6rnxncH2yivSCSdI//2v9MUv0vscvkR2A7W5p8naFnWl9dSSXs1fNeCWfE+qj7mWay3FiBriUeULJSWzeXUP5N3sc1v5lS6U3H4lthFZImx7ktSpIRpUZyqvqfVRTW6KKxoNaSCbV9ZWmamkGc0xJTM51zYmHg26GW6TGqLaeXqjvEBAL6xIamlveSZ6JBLUvCkNY/vCkN2oAmQ3gOHYBDJrwbKoc8CdAO93K7w9NyHu8UUdSuelUEAue7v709pheovCrjVbzvVGt7ZuM1viClrl3ZObKGe91ue3DyiVLbjxeX0sqKZ4VI11YTejfVxXew8iu1GNRXSbBV5XV6eJ8Le//W2N69dee62bkf7www/rDdZaAcAmi4dDbvn1cyuybjmYXR+60/eijgE9tzypnnTOzUqzwXAgV1JDU9yFeG+mqHgkoFmtCSu7rzGor48GXW+2Yn1Ufam8a+kSVECLugZcGxiboW790xtjcUXDYTczzfq1vdQ+oLYG28ispL22btWstoQCtlWZV9Kynkx5Bl5/Vs8Xi26gbmfRrQWMe0MwFm680UYwUkuLdM890oEH8hsGXyK7gdrc02RttlKsJ1NQNBJyWZ8rFLRVS4PL1+U9GRWLRfUMFNSdziro2SDc8jvkcj2VzWtSIuHyui9bcu8D9ps7yW1u9vKqpGKRiApeyRXJ7cS6nQ2PhoN6ellS6VzJrSqzPqwvrexXz0DOfX5Wa3kQbyvWxgzZjSpBdgMYjk1ws5PQtjIsHAzomRV9KhWK6kqVNwdNxMJ6dnm/MsWSkulGLezKalpjVKv6cm5PMmuTapmcz5f7qvfnim68bqvAbXz95NJePbMsqZb6mGvtNqM5Mf4rY8huVGsR/bvf/W7FbCza29vrLtva2ib6UICqYIvAbemWN8wMN9vxe0lPWqViSdliSW2JmBrawppUHykXxQMBzZ1UpxXdA/pLX0qzWhv0mlnNrjVMMlVUe39G7cms6mIhLelJucF+SAEX8l2pvHJ5Tws60+VNSUP1rr/q0p60lnSlFY0GNb0prv3mTnYz7GwH8hV9vW6Z+Yq+jIqFkuvHHgkGtefWLdphWtPYbFJWLEqHHSb9+MflQjrgU2Q3UDubjo3U8mVlMqNOWyWWybuZZjaAtoH01Ka4W0H2SiGtl9sHysX0UtHlc1siqmgkKM/6tSmgTC6vgYKnpljEFeGfWdarlf1Zd0I8l7cCfVapbMm1dguF5JaN2+dCoYA6+zPuPUFvOqdJDRG9uGpAjy3u1k4zWtyJ+zFDdqNKkN1A9Ro6WW2TNti2vcWkcjE9k1PRk8v0bKE8no9GA24fMptV/uyydjeGntYc17zJDSpYLS2VVSwSdu8B7D2ETVRrTkTV3pdxhfm2hpjqo+V9VDZ0gn5MkN2oxiL6RPZlW1upVNKnPvUpHXTQQdptt91GvF82m3Ufg5LJ5BY6QsBfMgU7Ax3WNpPqV89msx27VyXTaoxF3EZh0aBUF49qUjqvUDDgZn/bgNveANgmow8s7HJ/J5rjYT2zvF+vdA1oamNcKnla1p3Sou6U0tmCC3mbeRbwisoUi26Z+FatcXs3oUQs4nqydfZn3X/nnr098KS+XNH1TLU3ADZjbWZTXPbZ+R19r7aEiWphz4ASUesJF9z0Fi9PPCHdcIN06aXSsceWPwAfI7uB6jWazbUtox9f0qNFXSlXDN+qLaFcyXOz0Wwj0R2mNerZ5Ukt6upzbdey+bymNNUplc2qLxtSfclzGWwF9awXUFe/rQSTawNj2W8ZHAuH1JPPq1AoKhwIKB4Pux7oiUhAM1ri2nZygxZ2pVwR3nqt2vLzJd1p1xLOZq1Pro9ohs2QDweUiEY2vphAdqPKkN1AdduYdmzDFdynNsTUkohqeU/K7VFin/cCnlvVXRcNuPFwXzave1/qUNHzVAqUC+5WWI+FQlrePeD6pE/vz7rWqdtNrdf0QNDtf1Yo2sl329ckr60TifHrh052w6dG3fdgQ5sVPfvss/r0pz+tLcF6oz/11FP69a9/vcHNSJubm1d/zJ49e4scH+A3a89msz7mL67q11NLevTnx5e5mWkl63RaKmlyY0ypfEEretJ6aVWfW0pmIWtntK24bnPZV/RktLQr5d4cvLCyT6FgUIWCp3S+pFAg4M6QW082K5Lbn5ZlvRkt7bXdwXPutulNMW0zudHdp+R56urLulnpNpvOjtV6s+byJbUmyhulPLe81xXoZ08q7yRubzI2ih3E978v7b+/dOutUk/PeL3UwBZFdgO1t+nYUJaHNpPMZps3J2Ka1hjT3nNatefsVm0/rUmxUFBd/Tm3z0mu6GkgV3It3AZynpuxPr257tU2agNuJZjNLLcT64s7U2rvz6ngeW6Ge6koNdfF3L4n9hjWn3XbqY3aeXqzWzVmvVi3aq137WEWdaZUKNmsuYg7Gf/fBV269+UOPbu8z71vsKwf1YapZDeqFNkN1E47tg2NXQcL7padjy7udi1WbT8xW8Ft7djmTm7Q1KaYK6zPbI5rdktCkxIRt49YPBzUnLZEubWq57k+6T2ZvDuR/fSyXrUnyyfGrc3aimTa/e3ZdUaT9pvbpv3mtLqvtYlso9m8fNTIbtRKEf3OO+9cp3XKwMCAfvrTn+rAAw/Urrvuuk7f8vHwiU98Qrfccos7nlmzZq33vhdccIFr+zL4sXjx4nE/PsCPmuvCrrd5NGQzwqIuyG22mQ2ol3QPuHYt8bCnWCSklrqoUpmCGzR39ufcTt4D6bwi4ZCbxWYD4v5cTrPb6t0GJ73ZvAayBeWKRTdzrT4Wscnpbka6FdOntyTcmwf73ttPb1RLXUTbTGrQ7rOaNWdyvbad0uAG5Qs7+vXSqn5XpLdjbIiHNMMtQw9pUkNc05rrlLONVTZ2B/HOTuld77I/LtLpp0sPPCC1to7nyw1sMWQ3UNssD631ivVDX9De506QJzMFbdUcd63WHlzY4fY7sT1LLLMtU+PRkOrCIdd65b/2+YGCbHxvq9b6MkXX3s02Hu3PFmSNWGzTsfp4yBXYPS+gaCSglvqI9t+mVbPbEuodyLnv+eTibuWLJU1rimlGS50yhYLb9yRi7V6SWTdAt+vWZ90KBta2zS6tgLAOshtVjOwGqtto2rENsiK2nYi2SWu2Mff8jvIkNRtP7zyjWTNb69zlW3ebodltdcq6zUbLq75shZe1YrWieyQUdCfHbeweDYfcyWwri6/qS+vvT6/Qnx9dopsfX+aK69Oa4qqLht33eXZZUo8uKhfvN7uYTnajCoy60nTIIYes/ve///1vVzz/7W9/q3Q6rbPPPls/+9nPtNNOO43Xcbr/WD/5yU/qT3/6k+666y7NnTt3g18Ti8XcB4D1600X1GEzyUoqXxZLbtZ4dyrnZnx3p/Oa1VKnXKHkZqWnCuVNPS2Aw6GgIoGAgiG5Yru9EQiUpKeX9qgpEdPkhrhbOmYF7/5cSdl8QW31Uc2dXKcnlvS9etZbbha7LQsvlgrqy+TdmwDb7HRFb0b5QkBFBZRM57QqGXIboMTCYUUiIVesf9NODa4FjG10ts3kho3bQfxHP5L+9a/yhibvfCe/KqgqZDdQmwaXf9vg22aSWauVxd1pNwttUVdaTy1JquiVXGs2K4jXx8NqyUfVVBfW4q6UetNZ5YpSKitFI0UFoyEVS0EVigXlCgEFY9JApqgVXkbpQklRBRSPBF2R3NrARYMht8IsZLPcbT+VfMFtCh6LBDWpLqrWupD6s54SkbA6khnlbVPyVf3acUaTmr3IhjdMJbtRxchuoDbbsQ3XusVWiNsktVXJlDIFTztPb1R3Kq9VXSk3/rUT5TYGXpnMamlvRst70sq5k+JhVwy3cbi1crECuvU6t9Vhy7s9hUJBPbM86VaI2fsDK43bY1lp3b6uK2UrwtIayJb3N7N+6rvObNb20xo3rW2qIbtRS0X0VatW6dprr3XFcpvVffzxx7ti9ute9zp96EMfGtcC+mALl1/96le66aab3G7lK1ascLdbm5a6urpx/d5ALS0pswGrzRhP2IC5KE1piqtY8lQXta1AQ+oI55QIFJUKB1ybFgvc2a31bhOS/mxR6Vxe2aLcgLkpVqcdpia0pDujsGIKBnOKNUQ1kCsoqKCbAd+Xzcny2gr2Ty7t0ZSGuJ5e3qdtJte72ev1sfIbA+vPHg2V+6vasdpGKFZstzPry5MZF/pzJo2yF3qhIN1/v3TwwZK1oTrlFGnmzC3xUgNbFNkN1JbBAbj1SX2pfUCW0ol4RNFw0A2oba+SBR39WtY1oLpYVHXhgPKFkA6Y06RlyZxW9PSrty6qlfa+4NXZZgNZKWOzzANFxUPlmWv9ubxCASmTDynTm3En0QMBT5FQ2LV+sxYytploIBBUplDSK50pNxC3WegdqZzmTEpo7qR6N3C32XGzmuJqrAu7E+42eO/PDaw7Q4/sRo0gu4HaaMfW4kVcZlv7spy1Ps0V3LjWxrpW/N5xWqNW9mbUY0EcCKg/k9cDCzvdxLf+bEntkay2mZRwWf/Yok6199lM8fKmo6lcUfFgQJOa6tQ3kNPLHf1u89GGWESzbb+SaU1a1ZtRJBJwxfRswXP91W3Ft60Us2y2x3p0cZdUkpuZbvujzGqr27iNRslu1GoRfc6cOXrPe96j73znOzrssMPcZgVb0lVXXeUu3/jGN65x+zXXXKMPfvCDW/RYgGpfUmY9UHea0eQK1PFIyBW47Sx1IBjU3Mn1emFVn/qyBaVy5aViiUhEW7XFtbIv53qXW9G9MRHRivmdenRJt8tO68Nm/dBbExG3gajNjrOi/UA2p2V9WdcfLR4Ju69d0jXgZsfZMjV7A5Avlmeq28x369+6IpvVKx39rje7bY5SiliTl1F65RXpxBOlhx+WFi6Upk2jgI6qRXYDtWWwd6oVyp9f3ucyuyuV14zmmBsMP7us1y3HThUKWjWQc5t/t7y6abfNNHtp1YAN71XIeypKSkSlYtYy2Fab2QlyudttrpqN0q0wHo9ICeu3WiqpORFWPBZys9yt1Zp9L2v7Yu3b8iVP9fGI8oXynia7bNXiZrFbAd5OrtuMOyugW2Fhu0BgzRl6ZDdqCNkN1FZmJ23WdzLjxr9d/Vk11UXdDHTLxv50Qav6sm4mubVDtVXh0VDIfS4cDmplMu2K5v2ZkjpT5T3GLJND8lwb1a6MjZeDbjZ6X6ag5rqotp7U4PLWxvId/WlX22uM2d4oJcWjYTXFo8oXPTd+b4pFXQs2y+SBXH7j2qaS3aj1Ivq9996rrbfe2v17vGeer23MNjIAMKolZXZ9VTKrZb1p1zrFzn4v6BxwfVJzNiNNQbXEbVQdVChYVF3Y5paXXEj35nPq6Su5Qbr9p1v+jM1QC6k7ndNrZrW4liwretPqTRXcmfbWuog6+rOuf7rNek9miq7lS6EopWXtXRpULBbLAZ4taEUy4/qv2sy1nWc2uzcG1q91vf7wB+nDH5aamqTbby8X0IEqRnYDtbmyrLUu6nI3Vyi6JdsN0bDbVNT2F8mVSm4Wm80kT9RF1RiP6Oll/Xp0YY96M3kVS1I8KkVKrsbuCuhOoDwL3T5sLo3dz7ZCS+dd2d2dhLcHnjupSdlcUQs7+xUPhTSpPqYp20Z1//xOJVNZTWtJlGebZwuuJZxlevjVE+VDZ+itnulGdqPGkN1AbWW2tVQrdHma3hhx7VhsBfbcKY3K5otudZetJmvvK6ghFnZtVLOFol5c2ecyNBTw3FjdVm7n80UVCiXF6gPq6s8rFg4oUwgoEyy3XLMJbVaon9Vap61cYdwmxBXchqRWXO8ayLpNSq2YbivX9pzV4jLf9iux1eG7zGwZfdtUshu1XkR/7rnnVvdC32+//bTDDjvopJNOWv1mF4B/rTNgffU2C/S6VHmAa73Ri0VPbU1RrYqHlOkoaIVtLqKAZrdElcr1aGl3Vp5K6h4oKm8Db1vB5WasWd9VqVAqKuLCvOjOpkeCQbU1xtXfOeCWftvJMmvnUgp4WtqTcn1arZdqoRTQMyv6Nc02L4varHm5480VM65X2/Mrk25n8vWeGbfVLP/v/0nveU+5Hxubh6IGkN1A7bAMtZlrNgi2gff0xrjLces7boVzO9lsS7UTsZJrz2IDdzsxns4WtLSr3y3ftoF6MluSslJLfVh1Yak1EXO9UTsH8m4wHQ5Ynpe/Z11ECry6WXgkGFBJAZfLddGglvZk3G1Wg7c2ccGQzXSLqKUu7Fal2W2aUu9OiM9qq3fHvM7JcLIbNYjsBmprNXgylVfYwlVyvcezxaIaoiE1xEJu9baNnxvrIi6vrahuq8dXJjMKB2wj77Cbid41EFDOcj0W0kC6oHSu5GaTx6IhJSJBzUwk3NjcHtMK4jZ5zfYfm9Va7ya1BUMB7dDYpL1mtypnXxcJar+5ba6Fqm1O3pKIuVZso6r9kd2oYgFvE6Z49/f364YbbnCtVO6//363+ckJJ5ygY445RlOmTFGlSiaTroe69XRvspmoAEZkvdkWd6XdgPa2p5arsy+n3kxOkaDUmyloWXdKfZmSm2neVh9ym47ahqN2PTPMhPBXJ61rckNUW7UmXOhPboyqeyCvTL6gOZPq1Zcrup6ttpzcCvczm+u0pHvAzVRvqou4xz5q9xmuHcwDC7vU75akhbXHrBa9foepro/6OsGezdouw9ZgUrr1VsnaP3HiDzWYU2Q3UN190G2jbpvBZm3R7HpLIuxy02aXhwNBPbGkR8+t7HMzyqxVWrZog/GAa8PSn8mpK2Wnwf+ntU5qikXccnC7jz2mDawt77OFcqsXK8pbhlv22ozz6S22iWlB0UjQtWyxTdCsiD69Ka6Ogbz226bFtX5LRAKa1drg9j6JRaxXe8zNmLdVcG5vE7IbPkJ2b9nXBKjGTcCtJ7q1VhucFGaz1G1V+OOLu7WgY8Bt+p3PF1QMBPTiiqQ74W2ryixj502Oa4cZTa4tTN4raUlnRgO5kkJBT7NaE5reHFcqX074pnhEs1oSrjVL+0BegVJJLQ1xTUrYRqR1bka7nRjfaXrT8GPr9SG74SObmlObVEQf6tlnn3Wz03/+85+rq6tL+XxelYowB0bP+phaj7aVPSk9/Eq3FnWltbw35fq0WfHactgyPl+QwkHX0txtVpLOlpQb5vHszLfdb1ZzVFObE64Yb5udtfdl1JSIaF5rQoFw2LVsWdg1oIJ7IxHUws6U+34N8Yjri37gtm0u0F9c0e+Wmc+dknBF+b22btXMlsT/vqEdkJ0F/9rXpP/+l9Yt8IUtlVNkN1B9eb2qN62O/pwmN8Y0f1WfW/ZtrdMsO2e21OnFFb16ellSMZt9ni+4WW79maLaUzmt6h6QbU+SG+YEuM1YS0RC8qwHeq6gvrTNN5diESkaCasxEtSkprib9VYoldzmobafihXHA7KN0oqKhAKuUFAfDbsC+8zWOre5WSwUcLPcdt2qWdOb6tws9cAPf0h2w1fI7ol7TYBqY2PhJ5b06qmlPVrSk1FDxNq3eHpiaa9bpW090W3fMjuZbV3UbPV3Y7RcHLfVXnaC3Ga2e7axd67o3hM0RAOu77mtLLP9T+oiNhs97E6o255ndiJ7SkNMM1ri7v5tiZjry77d1FdPbG8I42740Kbm1EbsCjC8nXfeWV//+tf11a9+VX/+85839+EAVMhZcOttbqvKrJ1KOBRQd39aA5mCmzVm57GtF2ouVz77bTPMbfNPu+9I3OT0UnnJt81aC0aDak+mtTJZ7oPel8pr+2mN2qq1Tn32xqDouX5vs1rq3IYnCgQ1pSGqaDCgbN7T3CkNruWL9XKzmXBrtHLp7JROO0266SbpjDPKPdABrEZ2A9XDsttmn9lyb8vFZ5Z1KxgMuZ6nNmvcVoB19mddgd0G1NZDtTyAjmhlX3kwnvOsIB5Q1PM0YAPzV3PbTlbb/fvSOQXCQRWKJfcewE6MF4tSNCbNaLHBdkT5uOSVSjpg3iQt6UlraXfKFeztPYXNfk/EQi7Xm+vDaktE1d6X09zJCTdjvbkuopZ0Uvn3f0jRW25W5iMfU6yx0RXrAZSR3UB1j8XtBLf1Or/l8WVa0p1yG3/uOL3BLaK2grdtzG2ruS1XbQW4jatNJmdt1vIqFD0VikVlip5KJWvbJpf/fZGA23vE2rtYa7ZiKad4tLzhSdwmweULrqVbT7qgubmi64e+qi/tWr9YD/T1zkZn3I0aM+oi+rJly/TNb35TX/jCF9ap0lvl/stf/rI+/elPj8cxAthCO4P3DmT1Uke/YsGg0oWi64PelIgqlbXl1iGFghawniugD/Y7t8F0qWgBLDdLrVQonxFfW3m7MKljIOceyzYveW5Fr1L5orrTtmt4QEUvUD7znYi6GXQ24E+Eg9p5RpMWdQ8oHg6rL1NUX7bPDcynNcdd37Ydpje5ti42G69w772adNopCqRTCtx4o/TOd/L7g5pFdgPVz05+L0tmXCG9oy/rVnFZUdv2HrFcLnqeVvRmtLhrQJ2pnJYnM5pUby3SyoPxkOe57LZeqmZw81D32J7U3p9XfdhmpQdc3ttKssH72IzzqY11bkWZbR7elIi5Db9znSnVxSKuJVysFFBD3DY6tfYvQTXHI0rEwprkyb0XsNluDQ8/KO+UkxQcGNALP7xevUccqe28sFon9JUFJgbZDdRGwdwmgVmRenAsbnuVPLSgS8t602pORNWfTSs5kFNbQ0yJaMhNbLOct/vZ9p6DPSDcNDZPStr0dK98Atz2LrGT4XaTvU+IRzytLKZVKAZU8srFdvsetjW45ybHeerMZ9zJ8iW9GTXFwq73ejQUdLPRLc/teG3M3Ws9121F28MPqvGDJymQSkk33ijvHe8oP7ee1Ornxv6JqNkiuhXQbbr7cNPcbQp8X1+fu8/ll18+1scIYAvtDF6Sp+eXJd0mIzbgtllj79xzllvuZTt4x6z3abGobLag3sz/KuU2yA5aYT1U/vdwBtuk2xlxm8e2rKdfA9niqwFvPVY99aazenxJjxuU23JvO47GWFSTGyLabkqjO/tug38r6nvKaXpLnbaf3ujeWAwuZ49nPEXnbifvpz9Vyw7z+N1BTSO7geofiHcPZNQYDSkSiKq9LuJmntug1WadWZHaRtRd/Wl3otoGw/Y19jnbaLR8IjuvXKlcFHdtWoKS7S06yP0zINeWxT4fCki5os1Q1+qZbTtOb1Lu1U3Dn1rSp2S6qK0nJbS8N6u+VEZ92byioZAmNzS4++4wrVF11tolHFAiGlFjZ72yO+ygZy/5tpp3mKtSX9a9Nxm64TlQK8huoHoNLZjbpqLzJiXc7HMbA1uhfEVvSj39uXJ/85LUbPuJtcTduHh5MqV8sejG3DOaI1qZLG/4bePvdHHNyWtD14fbxLdMXgraP4Kea7FqY/BMLqdpkxrcXirpXF5TGuPusVd0D6jUVKeBpTm90tGvlkRUW0+qd63hbJa6TYqz429IFbXDDjsqct210qxZ6nl1PD743FbvcwJUkfIajlH429/+pg984AMjft4+d8stt4zVcQHYguLhkOtz+tSSXs3vSOmFVX3qSOW0tDuju19oVzwaUltD3PUqtWJ2JFKeV26D6cHZaOmS1J//X7F8JDbwth3Au9NF1z7NDA7crd/bQDrrzo7b0m/rr26z4/uyBdeT1Xq0WWHA3nzYGfbeVN5tuOItXCjvjDPU3tGrwl5769lrf6fU1Onj/8IBFY7sBqq3gL6wY0D3v9yh55b364WVfXq5PeWy1Nqr2Uot23TbNgizE+HW89Tavqzqy5RbrwWkVcmM2q2lWr48m83G1na7zUhbe6DgTnh75ccezH0rgjfVhV0h/pnlfXrolR7dN79bTy7r1/MrknpgfpeS6azbsMxWl9kMdSv220biO81o0txUp7b6/LlqjUiBffdV+ua/qrDVVm7mmw2+12jTBtQQshuo/hZsnjw3zn3klS7d9uxK3fdSh37/yBI9uTSpvlzR7UNm7VRmNESUzBTcPmEvrUq5tqb5QkD9uYIbSw83gW0wxl3b1VfH2TZGtx7q2aJc7lvRvT9TzvZJjXHVxyMayBf10sp+9aaL6k4V1N6X11PL+vTYom49urhHTy/t1aonn9Psz39aU2IB9e+6h9r/8GdXQB86MW9KY8xd2nWg2oz63emCBQu09dZbj/j5WbNmaeHChWN1XAC2MAtXK1CXvKI8154l5IrmTdYLLR7WimzRzULrtdAvls9gB2xAvZFbE1uUdg4UlYhIsbCUenUdms1sy+aKSkWDiufy6vOkxV1pNcbDqouX27jYirNs3jY1tT7tQbekrfEvN8n7zFlK1NUr8/YT9GRuG81qSzD4BshuoGrZyWRrifbMsqTa+7NuNVY8HNDrtp+q+khQ201vcnubvDi/UyusP3lXyhXMuwbKs8lLpfJy8Ex55fdqa2f64EDcBtt1oXKB3QtJ0ZC9byi5Hq3xqLWFkdvwLJvPu5YtNsAPhzzNbo0rEY2qFIgpX/DcSfEnl/Ro8m23aKtzz1LA+p6ffba03XZu2bfNWhu6xB2oRYy7gSpvwdabVt/KgtsXzCaOrerNuGK223+sUHQTy2zvEpuZvrgno+dW9OuVzgGl86+2VLM2bMU1Z5sPNRjlg+1Xg6/+OzDM/Wx/kjltCU2ZXK9c0dNArlfN0aB6BjLKFouaVB9XY11EizsG9Jr7b9de3/+iSg0NWvH8ywrO23aNMbf9206CczIc1WzURfS6ujpXJB+pkG6fs/sA8BcL4ZXJjNL5orZuq9OMxoRKpZTrfW4bjcQiYc3v6NPLqwbU0ZfSimReuXw5kDc063wkg/3Z7Oz56mAP2yx1a+mSd5uIWrHc2Ow1Wx5uS9hsifouM5o0o6Wgga5eHfbdL2rWX36j9NHv1FMXf13zJk9yb0pmNMUZfANkN1C1rNBss9G6BnJu1nfJCygeCer55UkdtP1kV0Bf1NmvZDqnoFdSX7boepgO5nZypN5r6/uexfIg3Erb5Rnv1k4m52bIWU5HQkF1D+Q1kC+51WNt9TFNbkioN5PVqj4L/ZJmREra6Xtf0uzbfq+utx6t4I9/rJZZ09zj24l8W/ZNCxfUOsbdQPWyNmaNsYg7kZ1M512OFlx7tqxyxZLCwaBKYc9NFrMV17bBaH+m6FaCWYa/uphM8VG0lRicB+5Ontv4e62qu504z+atFVvR7a1SKJQUVEANiaj6cp4awiGlC57yvQM6/Q9X6sB//kGdRxyljm99T1NnTFnnhDcnw1ELRl1EP+CAA/Tzn/9cb3jDG4b9/PXXX6/9999/LI8NwBaazWaF50WdA1rRm3Znj6c2xpTOlxQKSst708oXinp0ca8ym1o1H8bQ8bv9s1CwgnnQFc1dmOdKrmVLZ3/ObVa29+wWRSMhLe+VutJ57fTYvdrl9hv13MVXKPbxj7op7dbf1Xq5TW+uYxMTgOwGqpYNXG0gbpuG2vJsy+lSKKCV/Rkt605pcWfKzVR/aWWfOzlts8KsndrmsoG4zTqPRQJK5TwVs0UVS7bsO+9ax9QFA27D0rpwVPl8Sc+v6FU8GlZ3Kq3WRFy7P3mf9rrzZj36ua8q+8EPaeuGerWMxQsCVBHG3UD1sn1AYpGgSqmAa29mmRmJ2KbbYbUnM66taU8661Zr92cLrsBuk8ls75KhWZzeiHPhxcH8HlJQL71ahC+UbM+xtOKxiCbVRdxm36ViUS31YR04b7IWdaV1wCN36IB7b9HLl3xdnSec4sbbw+FkOGrBqIvon/70p3XYYYe5TUQ/85nPaNq08qyRlStX6mtf+5quvfZa3XbbbeN5rADGqS+btWixkLbNQGxDz5ktDVo1kFdnMq3uVEaBgDcmg++ReK+eVbclY31pz/Vcr4uGXAsX691a8kpa0pMuz4y/9x5ltt9bS950pP58yMGKzd1Ge0aC2nZyvZtR7/q5ep77YDdw1DqyG6hOzXVhNcQjioQC7kRzVyqvSDjoNiP753OrXGG9dyCnBV1pN8Ns7dlnm8oG3JbPNggvFIvu5Ho8VG7JZrPRvUjIZa+1eUkXi4p4IU1vimvKg/dp/mv20/17HarnrrxJjTvM086FEq3XgGGQ3UB1GhyjttlM70zOTRxLRMKKhUMqxjy314iNd4MBT08v7dFSr6T+dMFNLBtaM9+USHeT1l69tFVl9bYfiSeFbINwz1M+XV4m3lYf1Q7TW9weKs3336uZB7xebScfr6XvPkLxObM1peCpoz/niv1sHopaNOoi+qGHHqrvf//7Ouuss/Stb31LTU1N7k1yb2+vIpGIrrzySr3pTW8a36MFMC592V5s79eLK/rllbzyUq98Xum85zby7Mtakb3oBs5jNAZfhwW59UePBAKKhG0zsYBbJm5Hs83kRrcpWvfiZXr31V/RjvfdruTXb9CiubtoSfMU7RYLu0G4zbRb2DnglpP3ZQraPtDIbuCoeWQ3UJ2sNUt/Jq9pTXWuYB4MBNRYF1IyXXAz0T0voM7+jLqzG9+2ZYP7mqTKfdWLQ9q8xD0pGrFCelFewDI8pxktMTWlkzrsC+dpn0fu1qUXX6uB3XbXNnvtpFzRCgchdzIAwJrIbqB6V4C/3DHgCtmlUkDLk1nVR0Pq6bO2Z1m355jluY3Jl/RktDKZdX3QB2eNb+5YfPAdgfVVT0TDyheLro1qf7qoXMH2NLH2zAHlVrXrIz+6RNv86x+64Xu/09L6/dSfaNZWVmh3k99KmtoUd6vcrL0cbdhQSzbqnetHP/pRHXXUUfrtb3+rl156yZ1F22GHHfSe97zHbSwKwH8itjlYSepL55XJl3f5ntoUVV86p4FMTuFgwG0C2hCVenJj//0Hdwy3me6hoM149xQplc9sBwLldi67vPiEzrjuEkWzaV3/6W/oxVnba3ZjRPvPadUO05vc41hP2BW9Wbec3BDoQBnZDVQfyzjL53gk5HqozmyNq1QsaXFXxq0ss96pdjJ8rJVGOBHeHA8pFgkpWcy5JeHxQED7vPK0Tv/RRQpn0vrRWVeotPe+yqZzWtAxoJktderPFfRKZ8otax/sq8oKMqCM7AaqdAX4QFaeAlrc3a9iQdp1m1b9+dFlWtgxoIZ4eS8w65Xencq5li42TrY0H8tEtxXgfamCG2vb6fGStTUPhpTJFbX984/oIz+6WLFcRr85/9t6ZMq2SixNqikeduN0e99hX8bmoahVGz39Y6utttLZZ589PkcDYIuyE2Ed/Xkt6UmpY8CWlBVcf9WW+pACoZBabbl4XcQlrfUqf2Jp/xq9zDdXPCB3xt1tklIsbyxqO5IXSp6mNsa17ZSYDnjxIf3fdz6t5bvuratP+4KeibQo3JVSSyLmlrFbIcFm3lkxoa0hWu6h3hxjiTgwBNkNVBcrOlvLFDuBPJAtaEpTTPWRkOZ3pNwAN1u0GWUBxeWpJ1P+msGB+Fizx7U9VGLRkGKFoBKhsA546QGd/ZPPaeFOe+qHH75Y2ekztGtTVOGQ1JqI6DWzWtTRl3HH31ofW2dJuL0/sRl7lvEU2FGryG6geliu2Uah/3m50427bTa3tUd7+JVudQ1klCt5rg1bbzbnTk5ba5dkLj8ux2KPaq1cWuvD6hqwdjEBtdZFdMBLD+mcH56vVbvvo+s/dameVJNaAtLCjn7NbqvXzOY6ZfJFNSciaq6LrLOxKFALWEMJ1HCQ2xnvp5d1u9lrNhPMzoYPZEta2JFWNBRSW135jLQtKbO2LmNZQDcZ1wx9yA1Fm81mg3xP2f4BTW1sU/f+B+n+s76g/775WL20tFd9/XnFwgE9vLBTHf0ZvXHHaW4DFBuAN9gAvjmmHac3EegAgKplg1ZrX2YF7NmTGtTdn1bQ8zS9Oa5ELKxcZ0qBUtFtRBZRwQ2YbeXXeGxvYou7e9JFeaGCWq0dW8DTU/P20E/ee5ZWvvdkvbx8QG25ojK5guZMatDUpphyhZI7YR4NBTWlMbbOknAroL/U3q/SqyvThhbYAQDwG8u1F1cmtbwno4Fs3mXh1i0JJWJBxaNRecWUOjMFeUVPsbqIulLZcT0eW2De1V9wZ8LDhayiwbhe2nlf/frkc7T4PSerfSCrrs6MutI59/7BiuzpXEHhUEhxOyMO1Cj77wFAjQb5cyuS6k4VFA6HVMiXlLG1XQGbeO65f3cO5FQolJTMFLSyb3yDfFBvRnrDU//W775+irZbuUDbb9Wml99zsl7sSmsgU1DXQM7NPO9JF7SwPaWlPWkVi0XXN72tPuIK6HMmJVgSDgCoWtb2ZFJ91M0sf6WjT4t7MlrYmXJ90q1YPb0pqt1ntbheq4ND3fHaH9we34r5hzx1r3552UnafuVCtU5q0Q17vl23PdeproGsOzHfGItoj9nN2mt2q2a31Wmn6U3uJPhwS8KtoG4FdCuw26VdBwDAr5PXlvemXD/0vlxe/bmiFnenFQoHNKsloUwup0IpIHkBN0W8O5VVagvEnr2HOOy5+/TXKz+sSfOfUzoY1lNHH68lvVlligEVPblxt7EV4LafiV3apqOLu9LuZLfVFIBawkx0oEYN9lO1WWCdA1nFo0GFwlHXg605HlE0YmeZg2qIh7SyP+v6q463WD6rC+/4iU567K/6755vUHT2bIVCAb20qt8V9KPRkHsT0p/NqdnauURCau/LaFpT3J0IUDTslse1JKLMWAMAVDUrMDfGw1rZm1Yqk1ey6KkuFnYnv6OxkJb12Mnnol7t5jJuovmsvvCPn+h9j5SzW7NnaYfpDW6TtEw+r2m2/LtQUEmeZjQn1mjZYnk9tGXLILtuhXV6rgIA/GqwNdmK3rSeWNyrpZ0DWtadLq+8SkTUl8rq0YGslvZmlUxlZPXqaFgaq7lrdoLbktXK3GsvKK/LZ3XRnT/R+x/9q/6x04Fa3jRJ8UxekxvjKnpZBWTtZgKa1lSvxrqIkhl7Hlkt6Uq5ubjzptSrL1N0fd5ZKYZaQhEdqFGDA1Q7K17yPE1qiCmZtrPgnrJFT9Gwp1yppJdWDWhRd2pc+qgONa9zia668VLN6VmhS4/6pB5/+3GaEUvImrnaQNyWpwdKniY3xhQLSU2JmBpjYc1siWt2a8Id33BLwgEAqEbWhs36ky5YlXSrxbLWp7QuoimNcdeKrb0vp4FCYYtl95XH/Z+WH3e8pkaibka8rQ6LReKu52vQZs43xN1g2wxuImoD7+Hy2j5vLVyGK7ADAOAHg63JbNKXzd5OxMNuD7J0vqRQuqi7X+hwG4n2ZnKu4l0oSl5p+E28N4WNj+2xvPVk9yVHfkK/2+ttbgJdsCQ9szypaS1xTamvVzJbUqlUUt+r+4/1ZQrqTeXU3p9TbybviuvbTmkYo6MF/IEiOlCjbEBqg+8ZLXUKKahlvRlXRJ/TklC2WHQz1AfyBaVKRXle+Uz2eG1KZgaicXUlmnXmO85T19ZztYv1Nw8FtU1bQst7UgoGgoqGg5rZWqet2+rUlcprckNUb9ppupvJZsvjmLEGAKgVtkIsXSipN1tym4vaxmB20rk/W1C+WFK+4Ck1zqvIBrP73Hedp46522uvdFGTonI9U+dNrneFAiuiT22qc8f03PI+18Jlu6kN7n3ISJuHrq/ADgCAH9iJ42Qq5/YwsYlqtrGoFbUb49ZfvKhkT9aNsy3Pi1ZAt2wf48F2cT3Zfc47z9PSWXOVz5eUyxbUEg9r67a4XjOrTU2x8r4rvamsa+0aCwW0pCelqQ0xN3HNxuOW3dFwObeBWjGqInpra+uo+wt3dXVt7jEB2ELLy7KFkkpFz50Zt123C8WiMvmSmuJhNwi30IxFQwoHpYHM2BfQm9N9Ovee6/S1N5yilY2Tdfzxl7mwrsuXXCGgL53X/I5+hQI2yzyhllhILQ3l5eutDXHts3Wr5r569nu7QIAZa8AQZDdQ3WzgunVrQk3R8kDXVpClszbgLbpB81jNZBtNdts88Vgqp75CSerPuALBpERMCU/adlKdAoGgO9GdyhXVkiloZkvMPRabhwJrIruB6pEreFqWzChfKLrVYvGItUqNuLZrNrK2SWLZYkk5K6K/2nplrHnryW4TzpQUsp0SPVsAntOK3oxmtuS0osf2RMuotS7siugzW6w1W0lx22slFFQqX1J9bM39TIBaMKrf+G9/+9ur/93Z2akvf/nLOuKII/S6173O3faf//xHf//733XhhReO35ECGFOvdKb05JIetQ/k3GZkA/miGiJhrepNqXsgpFAw4Aa9xUxeqZzndvAeS/stfkrfvvkbqs+n9Ydd36xHZu28enOybEFatCqlulhIhVLJ9WZLFwaUzERV15dXJNysnaYn3Az0wRN8zFgD1kR2A1V+ItxmjhWLCgWDri1bqWAbg5cHzOO1amy47Laxtw3+g8GgXlmV0ozWuBLRiF7uTKkpFlZXXdgV9G0QbifIpzQUtMfsFgUC/9s8lFZsQBnZDVTXye4ZTXGX2TYLfauWuJriMa1IplwerupJuXHv4Gzx/BbM7kHW9M3eO9RHpIFsSYs60jY1Vm0NUb3SMaBe28Tc89SdzmmXGS2a0hDVqv6cgrYP6jgdL1DJAp79F7ERjj32WB166KH6xCc+scbt3/ve93T77bfrxhtvVKVKJpNqbm5Wb2+vmpqaJvpwgAlj/9n/64VVuu+lThWKJd31wkp1DuQVj4TUl87J6tJ1kZA7a96fHdtAD5WK+sR9v9GZ9/1aD221s84++hwta5q6+vNDW8ZYUd1mwW8/vV4DmZKa60JqiEe13zYt2m2rVu08s0kzWxJjeHRAdeYU2Q1Ul+6BnB5a2KEHFnTr8Vc6NL/TZroVlCpI+XGYgr6+7HZZHZAa60IKyNPUhqhmtdWrPh7V7LaEsoWi8oWSUoWSZrfUqbkuqp1nNKi1Pq6O/pw7AWB7tFgPdDYngx+R3VvuNQH8ltWDPdFfsdXVwaA6B7Iq5IpuQttzK3q0oq/oCtnjYUPj7kHBVz9sRvq2UxNqSkQ1JRFRVzqvcCCo7ac1aWZzTK/dbrLbe2VJd2b1CfDZbXWMx+FLm5pT9t/KRrEZ529961vXud1usyI6gMpnPUi7U3kt6hrQPS93qDuVc33ZLOjTeVt6Zquxi+oZ4wK62WnVAv2/+3+r7x74fp1w/KVrBHlkrT9KVgco2crwdF4lr6T+rG2UltaTS3vV2Z9RPGxDdwAbQnYD1cX6iC/uSqtrIOuK5pl8wc1msw3JNvrN/WZkd1udLeUun/weyBWVynvqTBXUlcppWU9aCzr6FQkGtNecNjcbry5qs9I9daUKau/Pur1NbABuBXQ2DwXWRHYD/p60Zh8NsZAmJaKa2ZxwrVNNdyavJb0DyhRL41ZA39C4e6i4m0BXnrzW3Z9TT39ORc/2JqtXJBxUKldwvdpt7G0rzezEN3uRoVZt9PvsSZMm6aabblrndrvNPgfAH4PvYqnkQjE5kFEqW94G3HK9+OqSrvSrm5uMlde98oSCpaKenr6d3vDRn+g7B5+gYnDNIvjaBXv7/pGI3HLw6c11CspzPduX9WS0IjnWDWaA6kV2A9XFBrLWHmVpd1p9GducU8qUysuyS1sgu0OvnvjO5T0FPCkaKq8ks/YsNti21WwDuYLak+UNyWym+m6zWrTtlHrNaK7T3Mn1bjO1WCToZrDZDPTR7r8E1AqyG/D3pLWXOwaUTBdcQNZFg8oVPRUKnpYn0+pNFZTOjU9DlNGMuwc1hKVYtDzuLgWkgoVzwNPKZEore7NKZQsKBAPqSeW0KplRUzykyfVRl/t2addtIt6yHmsJm3MnDoBqttG7AHzxi1/Uhz/8Yd1111064IAD3G3//e9/9be//U0//vGPx+MYAYwx2wAkky+6YnlzXUzJdEqW4XZWLWofEal/jKagx/JZff7On+rkR/+iT7zjXN2y8xvcZiYjGewJNxjzkxNR1cXCyuULCoVD2ro1qlJRWtHTrxdX9mm/hJ0NH495d0D1ILuB6jOpPqyBbF6Lu7Krs3OsbCi7LXVtMZh9JCIRVywoeDbzrvyxoCOlSDiktvqoejN5FT1pr9mtbnBtRQVr42Iz2diQDBgZ2Q34e9La4L4f89v7tbw3o2XdKf1/9u4DPNKy+vv4b3pJb9s7u3SkiyCIFEFFBf8KSpMmihVURBAVbGBBLOgL2AAVLNjAAoqKIiDSEYRdBBZYtmfT29Tnvc49OyHZTXaT3ZR5Jt8P1zCZySR5MsnmzH2ec5/z9Ooure3uU0tXVqkx/pqjXXdbLI+EbO0fUjDnKe8FFLZydC+gTNbT2s4eG3+qlS3d6q2IaemaDnfC22apJSMhNXcXitrs2r5XWrNhKhh1Ev3000/XLrvsom9961v69a9/7e6z23fffXd/Uh1AabMt07vOqtUjL7SqJ50ZNIjMFuLZMUqgL1n/gq669Sta0LZanzz6A/r9zoeM+GOTESkUKlTFZ3LS/MZKrWrtVXdfVqmMp7qKmB59qV3TquNa2FQ5NgcMlCliN1AeLAltCevlzZ16cUOPNnSmlRrjoq+RxG57mZDNyQ0Wa81k3AsIqyOviAfdMdoa3FREw0pGw6qKh121ub1vcSDgkgtWTW+3rXrNkun22oRqdOBlxG7AvyyuFduepDJZ157U1rTN3X3a0JXRWO+p3pZ1t718CAUD7jhz6ZyCoZByVmgXiygZC7uT4Pl8Xt2pnOorpGfXdbuYHY9EdOjO05TN2WuSlGu/ypBwTBWjTqIbS5bfeOONY380ACaELVJfMadGyxc36qlV7WrvyymXyrsE+litxRc3v6jf/egjer52pt78rq/rf03zR/yxybBVtwXcwtsmgNtRWT+51+02Xes7+tTRl9NBSxpdJZsFbs+rcEkFW5SzEAeGRuwG/M9inQ0pW7aqU//8X7M29Izt5JLRxG57vWAzVOzacuaJmCXQpWg4qBm1NlC00FimqTKm2RuHgNvrD0um1ynaP3CN6jVgeMRuwJ/sxLDN+7D1qQ3YXt3ZotXtvWrrzmhjeBwz27rutpPfVnEeCgUUDoWUyXuqiIUVctNLPNUnwvICAdXGI+rL5tTak9Lsugqtau9W3Yut2n1unWqTUVeJTo90TBXblER/9tlndd111+m5557TN77xDU2bNk233Xab5s2bp912223sjxLAmLOF7IyauBpr4i7w9abzbvG7vWLZtFLhqJ5pmKvPHXG2frXb4UpFYqP6o2Rb1NO5gDsDPqsu6Y5rWlVcR+wywz3mnuc2uDP41tO9NhnrTyqwEAeGR+wGymd7eChob+fcINGxsK2x21L40YC9ppAqEyGXUbdqtNm1MUVCYS1orHA9z4caGjpwq7stvu22JdcBvIzYDfjTwJPGq9t6VZuIaFFTpf67ss21Jh0L27vuthPgyXjYtXTxwiFFsnnVVYSVjEXUVBF1lem2a72+Kq7uvrSCsYjm1CWUy3uqSYbdSYKahO0kiw4qZgPK2agbCf/jH//QHnvs4fqg/+pXv1JXV5e7/7HHHtMll1yi8fad73xHCxYsUDwed2fm77///nH/mkA5ssTz+s60Em7Kdtj1Nd1e+694Qn/93nt1+DP3uxX1TXu9YVSB3NhQtLaerBLRkCKhoFK5vOY2Vqi+Mqa23sKwkiWNFdqhKalXL2rQ/IbkoIW4XdttAC8jdgPltT18Q1faJdIDwcmP3WHbPRYoDDttqoi7RXVtIuaSBa+YW6tkNOQq2Ib7XiyBbkn4lBsczmAyoIjYDZSHgAJa09Gn5es7XKvSsbC9sbu49O9OZdWXsRPbeaVzebX2ZFz71HlNVWqojGp2bYUWT6vQLrNqNK8+qb50XguaKrTX3Hp3ksBmk9k1Q8IxVYz6pfeFF16oL3zhC7rjjjsUjb5cLXL44Yfrvvvu03j6+c9/ro9+9KMuWf/www9rzz331NFHH61169aN69cFypElmm3xutf8eu0yq9pt09pWoXxO5959k372009qZfU0LZ22YJs/l1Wi27TvhmRYDcmoFjdVKBYKqqU7rRc39Ore5S3qTueUiEVcX3QL3AMX4gwqAzZH7AbKa3v4/Pq4ezuxHQVfYxG7K8LWgi2s6kRYFeGgptfGtLCxUtNqEmrtTenfz7bomeYulyAf7nuZW59wLV+sRduKll63s8xO9ANTHbEbKA+prM30yqs3lXP9w7fHWMRuSwLGw4W5JpbUT4QDSoQCqogElYzZGfqA/vNiqzI5TzvOqJbnBTW3NqmjdpupQ3dq1FG7zHA7zYCpaNRJ9Mcff1xvfetbN7vfWro0NzdrPF155ZU6++yzdcYZZ2jXXXfVNddco2QyqR/+8Ifj+nWBcmKV3NaHtL0no1zOU1dfRn2ZnAvu26Kxu1U3/fST+vC9P9O3DnqnTjrxMq2qnrbNx2dHYWvnrlROmVxeiWhYM6qjmldfoenVMWWzniri4UEV5wMX4js0VvQPKrPv094GpjpiN1A+LK51Zzx3AjkeCU9a7Lb8fTISUiRcaL9WmYypviLuKtms1UxvylM4HFA+5w1ZiV7c6m7Va7FIUHmPHWXAQMRuoDxYMtriXMAGeG7H0nSs1t12CLZEtmvL6XsB2/1t7wkolw+6lqld6Yw6etPqy2ZVlQhpp5nV2nNurfae36CFTZWukA2Yikb9m19bW6vVq1dvdv8jjzyi2bNna7yk02k99NBDOvLII/vvs3+4dvtf//rXkB+TSqXU0dEx6AJMdVbd9b91nXqptUcrW7u1srVXGzp7lc5uW0TvjiRcL7YTT7xM3zz4JOWC298XpjcndaaybqBJOpvVrrNqNasu4SrQbUFuW8wGVpwPXIjb2882d1PNBgxA7AbKJ4Y/9lKbnt/Q7eJ2fhsX42MVuzO5nEvqpzJZtXWllMlm5XkB9aazLnFuJ+ltcpltZd8SdpQBmyN2A/5nMbIyFnZZ67UdhfZl23b6e+xit710iMdDqk2GVB0NK+/lXYu46TUxpbN51+IlFAwpFAy4YeH1FTHX99xYkRrFapjKRp1Ef+c736lPfOITWrNmjUtW5fN53XPPPTr//PP1rne9a3yOUnJV7rlcTtOnTx90v922YxnK5Zdfrpqamv7L3Llzx+34AL+w6u2O3ozre7ayI+X6s/XlpO5RFKLHMil95i/f1aINL6k3Gte73vF53T939zE5vtDGP0zWj21dR0rNnVlFQwEtmValXWZW6eAdGrXzzKpCz9WtDCqjPzpQQOwGyoPFOKvyrktGFA4FXBI7PEmx24rWUnmptTerzlROOQVUk4wqm88rHAqqOmFve65Vy/Tq+BY/18AdZcPFd2CqIXYD5XHyu6Unrap4RI2VUVXEQq76ezLX3bbWDivgZqOFI0HX1iUWDqk6HnEzyey0t+0iiwYDml6dkJf33IDR55u79e/nmvXUqg49s47Wa5iaRp1Ev+yyy7Tzzju7hLQNFbW2Kq95zWt00EEH6VOf+pRKyUUXXaT29vb+y4oVKyb7kIBJZ9Ve1ibFhpLVJyOuSqy5M+XOSI/EkvUv6JYffVQnP3qbdlr//JgckwXqeKiQQLdLNGy3gwqFg+rsS2l1e59LHCSjEdd/bXZdhas8txN5Q31/9EcHBiN2A+XBYlwiElJLV1pdVimmgCIb4+fkxO6AKqMhVcXCSkaDbp5JKGDHFHAnwOfWJbTv/MLwsS1+rgE7yoaL78BUQ+wG/F+Fvrq9R/9b26lULq9pVVHVJkJbjdnjGbuLauJhzamPqTEZ1aLGpJqqo0rncqpKRNRYFVNlIqRAKKgVLT1a1dGn5q6Mlq7p0Or2lLrSOdfqxRLrVKZjqhn1ThIbJvq9731Pn/nMZ1yfNkuk77333lqyZInGU2Njo0KhkNauXTvofrs9Y8aMIT8mFou5C4CX1STCmlmTUEt3ym0tq4qFFLatWgEpvaVMuufpxMf+pM/89XtaUTNdbznt61rWtO0DRAefCZdCnlSVCLrKupwXVDwScIvySDikx1e2a0N32gX0vefWqb4yttVqNku6W7KBajaA2A2Ui+p4SDWJiFq7+9xure50vtDPdBJidywkZT1P6VROWS+nYGdA9z/X5k6AN1RE1VAV1Zy6pMJB6bEVbcrnc6qriGt+Q5JeqsAIsO4G/F+F/uy6bj25ulPPru9yMdlyWjYUPLOl+dnjFLuLqqJSPBpWT8rT6s4+daVDikcimtmU1LymqDLZvNKZnGbXJLTLjCrXUtVavliVekNl1BXjxVzrF0+r2rvc7m8rYrM1+NZOmgNTrhL9c5/7nHp6elwl+hvf+EadcMIJLoHe29vr3jeeLyL23Xdf/fWvf+2/z1rJ2O0DDzxw3L4uUG7ae7Nu6FdFNKz23rRqExFVxYOKbmXndFN3my6684f69e6H6S2nXTlmgTwRlsJhKRqzISYhzWlIam5dXLvMrNaSaZWKhYNa15nSyrZe/W9tl9Z29m3x81HNBmyO2A2UhxdbevXA8y16fFWXqwrL5qVMvjAYbCJjd0VEqohZT/RCRXokGFIsEtLazl6t7ehzw8HberJavr5bD77Qqt8/vkr/eq5Vdz/brBc29IzJMQDljtgN+JsVddnGqpnVcc2ojmtObVyRgLS1meDjFbtN3HqfVxd6n1uSP5XNq7EqruqNFeiNFXEFAkFNq04qEgm7BLq1aqtLxtxjktGQZtTEtNOMakVteDhtVDHFBDzbYzIKdubMBotOmzZ4CvCGDRvcfda3fLz8/Oc/12mnnaZrr71Wr3zlK/WNb3xDv/jFL7R06dLNeqUPxQaLWm90a+1SXV09bscJlCL7p26Bcnlzp15s7lFzV6/+vqxZvamMXmrvU1tXVqkhPm6flU/p6cb56oolNa1zg9ZVNYzZMSVCtjU94CaCV0eD6sl6qq+IuH5s1tN8XkOlKqJBpfOSl7c2LwG9btcZ2mVWzZgdA1BKxitOEbuB8ojhDyxfr7/8d43u+t96renKuWqY/ATHbpMMSRXRgPpyxZGhnmvf0lCVcLcTsZDr/bp4WpUWNFToqdWdesWcGncif6+51dp7/tgeDzCZiN0T95wAfmKtTh5Z0apHX2jRU2s71d2T1vMbetTcmVF6gmO3vV6wNjLxiFwiPG9xOxhUZyqradVx1SejOnhJk6ZVxdSRymrH6VXKZD01VkW1oLHK7YSzE/ltPSnVJmNuV5nF9GfWU4kOf9rWOBXelhfxQ/UpfOyxx1RfX6/x9I53vEPr1693rWRsmOhee+2l22+/fUQJdGCqs8W3BbmVLb168MVWPbuuU8+u63L/nrvTWXn2z3rAKbVgPqcP/OsXOu+en+rrB5+kbx/0zjEN5PbHpyZug0wC6s1aGA8pGMi5Ld7BQFB9qbzWtPW4LeEtvRl3bLvOrlWT7T8DMCrEbqA8Yri1cHlsZac2dBeKVjZNoI937C7Kuy8cVF/a4rZUGQ+prsKGptmsFYvp0oKGSlXHw+pJ5xQOB9x8k+pkxC2+AWwdsRvwN2srutecWjc/xIZu//vZXm3ozCgzCbE7v3HtHQmFFAh4SqXzSgWybt1treGqEwF19GaUCxQGjr7Q0qt59UmXQLcWLXZCoLk77eK/Xdcmo7RRxZQ04iR6XV2dS7bZZccddxyUSLfqc+uNfs4552i8ffCDH3QXAKPfTlbYbhVRdyqjrr60AsGA63eWSmtQMJ/R0axv/P4K7f/Sk7rqoHfo6lcdP6ZPd/GvR1uf9VaTqhNxJWNBzUgk3GAyW4DnAtLy9T16YUO3opGQ6pJRdaUy6ujNqrFqTA8HKFvEbqB8Yng2m1NXOquO3r4h+zGOd+weKBSxBbnnkufTa2JKRiKaVRtTY2VcqVzOtXbZdWaVqpNRzaiKKZOvG9QTHcDwiN1AebCcmSXSu1NZPbW6Q882d22283uiYrdVoVtstn7n4UjQVaPbCe+6ZNi1b0mEA0rnPCWyOe0ws1qpTM49xoaHGssfdPSkVREPq6Mn4+635Lq7iCI3TB0jTqJb6xQ7G37mmWfqs5/9rCt7H9ivfMGCBfQmB0qYDdm0qu8nV3VqZWuvUrmAgjZMNJfXwCZM1X1d+sP1H1YqHNWJJ16m++fuPubHYgE7GpbyNkw0bu1bQtZcSnWJsGs7saGrx/VUteO1y4xEVDtMq1LAC7g+7gBGhtgNlId4OOS2gN/7TLM7FW3xUxMcuwcdTzCopirrmyrtNL1akXBAr5hT66rWbJHeVBlTPBpSMmqV55Ehd7ECGBqxGygPlj/7z0vt+vNTa/TflR3q6Ju82F0Zs1aqAcVCYYVDAXWlsm4waGefp6yXViqXd7vDF0+vVktXSolYRF19GS1dnVUml1cyFtaq9j5lWzy3u2xRU+W4HStQFkl060VuFi5cqIMOOkiRyFamEAIoKbaItf6kfZmMauJRV/G9Kp9XLptR1rLo6bTSobA64pX63BFn6x+L9lVbYvx6GPZkC3+AevoyCgZDigQ8re1KqToWUiIaVHufp2Q0rO5UTp09aa1u79WMmoQ7Uz7c9lYAgxG7gfLR2ZtWOBhURSSkZmXdfbHsxMbuIqtWS2dzmladcO1c5tZXamZNQvFoRMFgQHUVMRenrYJePYXXIMRtYGSI3UD5tGJ7YmWrVlkBWzbf34JtMmK3DQK3XufTqmOuoq02YW1dgvLyFs9DakxGlfc8pa3y3HarZ3PK5/MKBINq6UorEupTdSyq6TVxl4C3WWXAVDTqnuiHHnpo/9t9fX1KpwdXhTI4BChNtniNRYKqS8YVi3ZrVXNavZmcbCzZvLXLddWtX9HNexypH7zyrbplt8PG/XhsK7pVwtdXxjS3LqG0l1drZ8ZVzNtwsng47CaA25DRXWdWa4bbJp5wW9DsgCzgoAABAABJREFUBYltHQMwMsRuwN8sGW07s2LhkALhoMIhacGaFyYsdg8cm2Kbx2LWzsULuG3gVbGI4tGAe78NBV/fmXK7ybrStgD3XFJ9cVMlcRsYJWI34P/YHVDQFazlCsNEtGT9xMXugVJWwBbIu11kbb1pVUZjmltfof+t7VQ+lVEwFFJlNKhELKyKaMgNDV3T06e+jKdkJKSegNxrEFOTtII8imoxNY06id7T06MLLrhAv/jFL7Rhw4bN3m/90QGUJktQ2/Yr68vW2ZtTdyajY+69TZ/86/e0oma67l6494QdiyXRY2Fpbm3CnfVe356Sp4DWtvcqEgoqGgppelXcLbr3mlerpuqEmxxui3N7QULvNWDkiN2Av9mW6550VoGgVBsL6XX/vUMf+sM1Exa7LWZb0Vk4LEXCIXme1NGX1tqutLyAtWSTYrYAj9nrjMLtwhyWQlKduA2MHrEb8P/auzYZdsVgz68P6dgH/qBPTcK6uygZiygSDGlapc0jCyuXz6mpKqqdZlSpvS9daP8aDGrZmi71ZHKqjoW1srXHFb5VxcIuzk/P5jS3Ou7asdqwUYvvhe+THWeYGkadRP/4xz+uO++8U1dffbVOPfVUfec739HKlSt17bXX6ktf+tL4HCWAMWHBraEi5rZeV3t9+vRPL9frlt6jG/d6vT5/+LvVF4lP2DNtG9HthHxHX0ZeIKBM3pPnWX/2gKoSEdUlI8p6nuuZnpdtCc+5hbhVtFmgBjByxG7A32zb9NyGCnW2duhdP7hUr3rk7/rFPq/XZ147MbHb6ueq4gFVxMJKZ+0keECRcFg10ZDWdfbJ6tDj0bAaK6KaXV+hfC6v7nRWXoenUChI3Aa2AbEb8P/ae5eZNXrppQ16+68u1yH/uUs/3/v1uuSwiV1324nwxmRIM2oiioelHWfWunjdlcmrJ53TvLq4K2aLh4NuTR4OBvRiS6/m1MXU2ptWwPNUUxFzw0Vtl1lzd6EbhV2z4wxTzagzUb/73e/0ox/9SK997Wt1xhln6JBDDtHixYs1f/583XjjjTr55JPH50gBjElLl9l1CS1qrNIqNx08qHOOu0i37/TqCX92bTt4VSKsnOdpZk3SDk7ZrIVvz20Pn9eQcFvfFjRVqKkyqrrKmGoSkf4z3QBGjtgN+Jttm7YYmI3ElFFQnzn50/rDkoOU6p2YHaC2c6wqHnVt4TzllcnnFQt46svllQgHtdvsGm3oSuml9j619GU1syrmkuc1yYhmVCeI28A2IHYD5SFg7VRjEV104qf0y3mvUmaCv34sJDVVx1UZD6siHlVvOqdUPK/959epuSvlYvu0qoRi4aBLjHf0ZlSdjKi7L69cXsrmPLU2d6k2GVVlNKxcztqrplxBHDvOMNWMOone0tKiRYsW9fc/t9vm4IMP1vve976xP0IAYyeX04Krv65DZ++k+8Iz9PH/+4QLfv2NTifoj44VkltPNU+ewqGwq2yL9IRcn7b6yoTCQatKD0gBT/9b0+kGqe08s8a1drF+6DZklG1jwMgRuwEfy+VU+7UvaYed99LzM3bVte/7vJ5Y2aFMNifrTloYMTq+Ap7UUBFRLBpRIpVVVyqjaTUxLWioVN6TNnT2KZWzvqlB9/aM6rgSkZBL/DPDBNg2xG7Ax3I59V76Oa2YsZNWztpd/3j3pVq6qkv5voltf2wzTZLRkCtKy+SlDT1pre1MaU1Hn9p6s2qoiKq+IqZYOKNQMKimyphm18a1Q1OlWrr7lIhZCzdPT6xsd0n25c3dmlufUDwSU0t3rzr7Mm6OGTvFMVXYzo5RsQT68uXL3ds777yz641ePFNeW1s79kcIYGy89JJ0xBFKXPYFzX3pOTVVJ13Ai0WDhcT2BPyxqQxJ9RVhJSJBJWNBNVTGNaM6pnBQqowGtNPMKv3fXjO038J6za6JaVFjpTszbtPBjSXQn1nfpRUtve7abgPYOmI34O/YHfjsZ9W0fJlrdxYMeAoHPJfYDm9cII83awNn57a7UmnXJ7UqEVM8HFF1Iuxi9YyahOuX+sKGbq1q69MTL7W7Hu4sqoFtR+wG/L/u9v7zH7X0ZrSytc+1Kh11Am4b2Un2irDUVGEntAu7v63tmpcvJNUbKmPq6k1rZVuP/v1cs55Z16m27j7XPm52XYUWNlVqUVO15tRXuI+b31ChVy1uUmNlVBVRa+2WVzRkQ1PzLvHOTnFMFaP+N2wtXB577DH39oUXXuh6osfjcX3kIx9xfdsAlKBbbpH23FN65hnpr3/V6tPfq5pYyA06sb5oNXFLaAcVHeMvG9iYnK+IFL6GVa1Z0LZFdSwU0MzqqJKxkNJWvRaPuq1ifTlPTZVxhUIhpbJ5LWysdFvH+rI5N7ikOKjMru02gK0jdgM+dMst8vbcU/n/PaN1v/2DnjrhTK1o6VZrV8YthlNZKTUBSfREWIpHwupNZ921LbxDQfu6eSUiEYVCAb1yYYOmV8fVl81rWnVMqWzOxXsW1cC2I3YD/l53v/DzW/XvY9/l1q1V8bDCFjwnQHDja4OcZwnzsOLRiIKhgOqTITeg3OJ5Z29K7b0ZPf5Su+57boN+/9hKPbGq0w0yL7IYvripUgsbKtxclljI1vMJ1+rFHmWJ9jqrYo8EXdtYYCoYdTsXS5YXHXnkkVq6dKkeeugh1xf9Fa94xVgfH4DtlUpJH/2odMgh0g9+oLZ4ldY9vV7dWU99mbyCgaACkYCq45buTqu1J+u2elmXl+0VC0jhjUn6SCiiukRYPdmcYnlr1WLDRANa0dKjcDisRU1JxUNh5b28qmMRNVbF9FJrrzxPg7aI2WDRLQ0Yte1mVqHOpHDgZcRuwJ+xO3PQq/XE567Ui15c9zy4Qi+1dGtdd0rZvKdoWLJzyWO9JLdXA0ErYctJ8Zi1XwursTKuqkTQVZvvPKvWtWyZXZfUgTs06L+rOrS6o0+hoE01CbiSdeuZHrD/WFQD24zYDfh73Z3PRRV7dKX6MjlXHGYDO235msuOzVp7OJbgtqW9xeVoNOSGgTcko6qtiGl1W597/9qOXq3rSqm5I+seZzvO1nf0uDW0ractftvFWrJZMt12nBXX1/b+rlT3FtfkQLna7t92GyhqFwAl5sknpYoK+0cq3XOPNH26S1z3tvW4reDzG5Jq6Uq5bdmmJh5SIlQ4a93anVXfdvZJt/V3NColIxE35bu9L6XWXlv4W0uXqGoTMbX1pFUZDSnignZai5oiqopFXCXbgoYKt1hvqIxqQWNVfzXbDo0VWtvR5w7UAngxyBcVW74wKRwYHrEb8Efsbo5VKdPap3Bnr3pSGRcX6yvi6kt3KbMxTo9ld9UZyaDre26x1QaQWU/zjr6sqhIhhRXQtOq45tUltNP0KreTLJPzNKc+qZnVcdUnI25YWTQSctu8bXg4gLFD7Ab8te6OtXa7Nigt3Sl19qRdW5WWrpBeaulzO8nGSmDjxRLz0UChBZvtBouEgqqIBLWoqdKtv63qfHV7n7vfy3vqSecUtr6qntSTyrtd4Wva+wq7xiPBQTPILJlet3Hfur1GWBwIqCedcZXrdm2KjwXK2Ta9un3ggQd05513at26dcq7qYQvu/LKK8fq2ABsCyvd/v73pXPPlU44Qbr+emnGjP53WzBMxGwpbNXgUi5f6M22vistmw4WsbPJUSmb2r5hZfaXoTtl08Bz6uzNy3aGWYxO56W23rSCwaAaK6JaMqNanamsYsGA2yb+UmuPUjlLqqc0ryHpEugWtIsV5ms7+9wgFBtY1pXKaQd70WAnBzaeGbcgXmz5YmfH7f5iwAemMmI34L/Ynei2eJmSjQZJxiJam+qT8jlVxCJSPqOxnk/Wmc7LC2Rc5flBixtc1dyqjpTCgYDbFXboTtO0ZHpVf9WZtVorLrJbu61fel69qax7nTGjOjG2BwdMQcRuwL/r7mQ0opw8V0RWWxXX+vY+tx6uiEsWzsdKfcIqyYNKZ3JuUGjGC6giGtDc+grXas3mkHX0Zt0A8Fg4pPbetGvLZu1ZguGgstmcptfEdMiO01xuYNmaDtemxarMrZ3LpgPCi0l1s6q9WLyWGvKxgKZ6Ev2yyy7Tpz71Ke20006aPn36oDNNnHUCJllrq/Se90i//KX03vfaWa3NHmJnwOuTUdUlw6pNRLShu89VkmWyVtUdcD3Scvnt3x5uBXK2tu9K5RWwsvQB29bCoYBiQbkkeTwSUkUsrN1m1+qZtR1q68lrem1C4YA0oybeX4FerDBf196r5u609pxT67bFWVLdkunFynNLzG+t5Qsw1RC7AX/G7mI/0umVEYWU18MvtigW9JRu7tKGjrE/lN6sFAkV5pO81JZSPOKasrikfTJm8dRTPGxBXe4ktVWged7LFWh7z60b1E4NwLYjdgP+XndbHJxTk9AT4TYFlVdbb6EivWuIBHp449p5qM3gFk0LkXZzrvd53pOF5op4RNZMrS4Zda1S95tfr11n12p1W6/WtvdqRUtYaevbaon3ZFRLpleoJyUlYkHtNrPa7QBv78u6gaEjKUgbOK9sXYcVuvXSUhVlb9TZpW9+85v64Q9/qNNPP318jgjAtslmpVe/Wlq9Wrr5Zuntbx/yYe29WdcOpaU7qw1dKXX35dXdl3UROBSSrLuLnS0fq9Rzb64wlMxy2XaI1p+tPh7V7PoKHby40R1P3rNke0bRsA0hi7pe7fU1MVfFVjw5VwzSs+qS2tCd1qr2XjVVxd0rjYGV5zZR3BIOLOKBlxG7AX/G7mK1V0tXnx5c0a5la7pdqxQ7iZweh8Mp7CKzqrlexYOeZjdUqjOV0zPrupSIht0J947enCoTERd7rRf6rOq4qpPR/go0dn8BY4PYDfh73W0x3Nqg2Yno9t5OV/jVlfE2i9+22o1HJeuKkh6QRY8GbZ37ctu2wqnswu3i2/axVvyWy3mqiAZVWxHVHrNrtHhaleorY65YzoaB7jitUl3WvmVlu2bXxjW9Oqo59RVu3T23Punat9VVxjSzNqnmrvSICtLsfcXiNWsR29uecxXvw1WwA+Vg1Hky2ybyavuDAaA05CzrnZViMekrX5H22KPQj20Ylly27dm9mYzWd6eVzuVd+xRr3RLKFoLyxsLxMeECe0CKh4MKx4KqrbQq+Ihm1sTd3xOrSg8Fguroybjg3VARcZPEd5pRPaiKrRikU5lcf/9VG3DiBpukXx5sYtvmWMQDgxG7AX/H7qVrOvX06g43M6TQtqwQX73xODSbjZYpnAS3Fm+WLM/l84qFQu41gyXO5wQDaqiMKZv1XG/09p60nm/ulFSYYcLuVGD7EbsBf8duEw0H3bo1lcmqvSfbP89koNDGS0VMCqalUECygnFv4zZuS5LbLm0rPCvu7HZrd7dGlioTsUKCPpVTMir1ZTzXJnXnGdWKR0NuHW1tmGfWJpT3PE2rSigRDbqkeygY0oLGCpc4t7X4zJqEK2qznIHtPLO19spWO4FvA80La+1inC/ulrPHtvdk1N6boaUqyl5wW6aEf+c73xmfowEwOitXSkceKZ13XuH2m9601UBeSEYH3cQRC4q2pavYlKkY0/PbkUS3Pyr2+UIDztRZMbkNKFs0vUozqmLaoalSu8+udUPIquNR184llctrfae1lsm7gWU2+HTgIrwYpK0FjG0X33lmdSFZXlGofptbn3DXbB8HNkfsBvwdu42dKLbBnZ19WVdxlhynbmUWecNhqa27Tyvb+tRjC+lIRLUVYVcBbyfFred5VyqrcDigde0pl1hv6S60XbP2awC2H7Eb8H/stt1jL7R0a3V7ylWZD9muJWgtWWwdbbPJAi7eVsUtwV14vK3NLYFePHleXCFHw7a2LxSZRUIBRcJBdfSl1dGTcr3PLRk+qzbp1su21i60ealysdzmiy2ZXuN2klkCvVh1XtwBZx9nbz/b3K2nVnfq7mebtXR156A4P/CxVtwWCgVpqYqyN+qX3+eff76OOeYY7bDDDtp1110ViQzud/jrX/96LI8PwHBuvVU64wwpHpcuuWTEz1Ptxirw3WdWq6cvq3ueWaf23pwCuUL1Wdwmeoeknu0oRa+OStMqY2ruySgc8NzZcSub8/J51VUldNDiaW7oSU0yot5Mt55d3+2SAp29GaVsKJkl9gMBd1a8mEjfdCp40XD3A3gZsRvwd+y2arIdp1eqL9OhSGEuuJq7euUF8uoekLO2SDjSNi+hjQtzW5BHA4X5aDav1FNA1fGwQoGAYuGAZtZWuAq46TVxRUIhzayOaV5t3C3Iq2Jh9WWzqoi9XMnGQG9gbBC7AX/H7uIA7raetOszbi1ObfbIptL5QkwO5G3OiNTanXUJcUusF+N0sQLdxEKF9quJcMi1i7FkemtPzj3QU14VCVtjF1q42jrZ1suWILcB4SZSE9OO06tcrLfj8/JWuR5yO91MsdK82E61MhZ2O88q4lbR7g0Z5wdWpTMXBeVs1En0D3/4w7rzzjt12GGHqaGhge2awESzyGoTwK+6SnrLW6Qf/EBqbBzxh1tAtCnddqa4sTKqRdOqtGx1u/J5q3DLu6EkNjU8F5BS3vB/NLJb2d6SjEdUFwgotHEreNKGl0yrcn3aunrTrsXLtKpCAN/QnVJnKuMq0m3r2oqWHvdioS+Tc8dqLMAXA3IxsW6V9HYmfGCwZgs5sDliN+Dv2L2wqUKH7TLDxbzV7b2ux2nM4l7QVswZt5i2xfZIE+i2jC7OQbGIGrXYH5Jm11aorS/jquKsSi0ZDWvH6dWqjofU0pNxcfnZ5h71Zj23GLf3572AgqHAoEo2ANuP2A34O3YbG85tJ6AT0ZBr7WJt2TZdYhfjsLVqS0ZDSuVyyuby7sR2zCsUt7nHhK2yXcrnCie9q+JhTauOudkkzzT3uHW3tW1NpfNa21nYJWYV4pZIt3WyrcWL62ZbR1uVua3Tu9NZ9/ktpgeDqf5+5sV2qtaqxXae2Rw1q1wfKs5T2IapYtSvcm+44Qb96le/ctXoACaBJZBtB8i3vy29//2F26NkQdN6kK9s7VFHd8adWbbz1vY6oTtbqEizxXXQgvQmH2vJ81hwY6+23OD3WwLdTnBXRCNu+1ldMqx4OKC+jFRjDdrkuaEjK1q6NSPv6blQQLNqE9ptVq2ioZCWN3e5RH5VPOaGni7f0O2C/8tBffCQEksm2JYyC/4MMAGGR+wG/B27bXHaWBF1A7VXtvaqO5VVKp93gz4tgZ4aEIxdNdswJ7steZ7Z+D7P9TwvHIrNIokEAqqIBRWLJJTL5RSw1m8B6aWWbtcntTebV0U0rN5MTmvae12V3PyGCq3r8Nz7bXcZ1WfA2CF2A/5fd1tBmA31bOlOKRgIKJftVtcmAToWlqpjEfVkMi7BXpuIKOr6kduMkrx6OtIurgc8e19ITZVRTa9JuIT5tJq45jVWqjdng78zrtd5fUVUr5hd7RLxNq/E8yo3K0qzE/K2hm6qiql5VZ8CwYCL6TZnrFhpXqwutwp1a8c6sCc6MFWNOoleX1/vWrkAmEAWQe3Mty1ozzxT+trXtuvTretKuTPO4VBY7ams20LWVxz7bQtsTwp7FvQjaunIKKXBi3M7823sLLidNXfR3tbaeakibov8mDvjvr6rT5FQUNFgUC096Y1fM6imyphb8FuQthcGTdVx7Tu/TvPqk3qxtUe5XF592YBm1SRcon9gUC9uM3MDTHoz7rG2jW1gwAcwGLEb8HfstpPGazpT6uhNa3VHr5tn0pO23uiDE+hmQDjfjK3brR2MVZ27wd8bK85td9iMmqTi0bDaerKF/uuZjGq9iHrShYo468dqsT2bzrsFun2cxV7b2VasdAMwdojdgP/X3ZZw3nNendJ5z7U7TWeyWtGS6l9fWyy2WSO27q6vqNJ/V7Ur5wU1uzqiYCisNW29akgGXW91m2RWXxHWbrOrVZeMKet5ioeCSmdybl7YqtY+VSdC6svk9cgLrbLgPr8uqWfWd7shobZGt5htifFilbnFcZtzYsdhbw/cUdZfXU58B7Y9iX7ppZfqkksu0XXXXadkMjnaDwcwWm1t0nveI918s+3rHJvnz7PtYCFl8zlXPV4ZDaiv9+WNZcXBolZZZgvnDd25/opzy58HAyHVxMOaXRNWOptWLh9UQ3XUbSW3qrZwMKjmzj73IiSTy6o37amhIu62gedTOVVGQ3pxQ4/m1iU0qy7pXhTY5PD9F9a7hLhVqrf32sdlNwvqNhl8VXuh+rw7lVF3KucS9LaYtxcHADZH7Ab8HbvtJHEyEtLM2oTbGm63ezOW1H550FjRprcHspgfDdqOMRseJgVCASVCASXjYdUkwuroyak7nVFjZVzdaevjmtLMmoQWNCZcct2SAQ2VMe09t1Z1FbFBVW0AxhaxG/Bn7B7YctQKxrI5z1Vy26BP25Xdnl6vDV1Zt762hJxVj+cU0Mp2m3USUCab04ZeT8FAxiXSoxFrAeMpHLRWajG19Vr7lYA7iR7wPFVlctqhvsK1dqmMhfSfl9oVDHjK5QNudokNAg8Gg5pTX1h323FZbC/2MC+uoYnpwDgk0b/1rW/p2Wef1fTp07VgwYLNBos+/PDDo/2UAIZz773SSScVAvovfiEdf/yYPFduW9n0SnX2prVsTYfWtXubLbJrY0EtaEgqaQNQ0r3qydiYEqkiEVRlPKz6ZEQV8ZCq4xWuwtyS2NaHzf5r7U65anZr0RIOBZQPee5MuA0ytUo2C/yFAacJN03czojbItwS583daZfgr4zL9U/fqWpwT3SrRLfqcxt+8nxzyiXmbUipZ5kEAEMidgP+jt0W/yxWhgMBV0Ve2FJeaOUSHDB0LLxxW3jWKtS9IRLo1qrNeqHncy5R3lAVdSe/baFtJ8CTMU+BYNTNRomFQ6qIhtSVyqi1J6T5DVXac26NZtYkmUECTABiN+DP2D2w5ai1cbGhojZ8O+95WjytQsvXdyqX61IqK7d+tsS5tVq19XIgEHRtUzd09bnk9vzGpHL5nHK5jKriUddbPaiAQiHrVZ5ylejBvsLEcUuUL13d6dbT+86r0wvNXXpuXbfmNCRdMn9lS7em1ybdawp6mAMTlEQ/7rjjtvFLARj1VrJPfUqaNUv6+9+lBQvG7Am0LVl7z6t3W8eWru7Q+o4+ZdKFBbgty6tj0pIZlZpZHVPGhpps6HVV5nYJbOzTVpMMu9Yt+8yu1upOS3wH1ZvKuxYuVjGejIXdC4bqmCXIQ8rkg+7r2TA0e/Gwy8wa7T2vUMm2aW+2YnsWq4Svr4xtdvy9mS4tXdOpdZ19bpr4gqZKxUI2qKWwiZ2Bo8BgxG7Av7HbYppdrLpsXkNSc+or1dnXrnS20M7FKsuTsYBi4bBCQU+5rKfubE6p1OAEumvfEpAS4aBbsHf3ZZTK5lQTjygWDWl6TVxLqirV0lX4wLl1QTcAfFVHn2vfZp/bYq0di8VrBnoD44vYDfgzdlt1d7HfeGdfxq2nn2/u1rPru/VSq+2utvVxRJGQ59bQ1v7UisRCCqizL+2qyCvjEdfStKsvq8bqxMY1c8gVktl8FNuRbYnx6VVJRUMBt5YOBUPq6k1rrc0WW9+psO089/Jud3dFLOxaqFr1ObvHgAlMolsrFwDjaOVKaf16aa+9CmfBa2ul8Kj/qW5R8cyztVBprIypsTqu3ubCAE9baIfDYa3rSCsRTbnhoZFwSJFcoYo8ncu7yvOqeMht9e5I27DQwpTx7kzGnRXP5T3XO70uGdX+C+s0sybu2rfYon1Va48WT6vWTjNqNKu2YlCPtYG92Qb2YxvIgv6MmrjWdfSpPhnVS209euzFVu0+p9adrW/tTmtNR6/WtPcVquM39n2jlxumMmI34N/YbRVtzzZ3uwV5Mh7RrjMqtd5apslTyoZ9RkKuV7mdvE7nc8p4OYVyUmUk51q+BDYO/bbKtlC4cNK6rdd2jAWUz+aV9vKKKaT1HSl5+YBL1NvrA9sdZsO9rY1LMhZRc1dGD69oc68bhhr2DWBsEbsBf8bugWva6rgNCQ1qXUePO3ltlelu51gwqEDQc8VpFqe7Up4q4mFVpm33dUh1FRHXes0q0XeaXqXKWMTtBm/pyWh+XUKdfVn9a3mLosGA6qpiWthU5YZ+RyMh7TK9SqmcvT4IKhyyHd7Wsi2qOXXMLwG219hm5gBsn9/9TjrjDGnnnaV//lNqbBzXZzQYCKo3k3N9xu31QjZbeN0QD9l28ZBabQCpa7dS6JFuPNeuJa3ZtQmXKF/d3u36vHWlAnphXZdLptdXRnXAogaXPN9tVo2q4tbXPKDmzl7XesVNDU8W+q8OVJwAbmfvt9Rj1ZLluXxe6WxOO06rUjgc1MzqQtsX2zq3rr3XbWPbc06tSzAwcBQA4NfYPbCiLZ/Pa6cZ1XqhpdctoHP5jKoSYYUDhQV3OhdSNptSNpiXt3H4ty3OYxGLuxEpYC3ZIrJT372ZvBv23d6TVS4rLZkeV10yon0X1KujJ61wsM/F73Brj7p7M6poqnDHYa8bisO+ia8AAF8ax9g9cE1r1eJW/NXcmdEz6zr0YmuhcM0u0WDQVZV39GZUHQxopxlVml4Zl00js7WwtW7Za26d9lvQ4Na2FoPrKuJqqIioOpnVQcEGRcMBza2r0PyGpCuUsyGiNlMsms+rPhlTVSKiDV1pxSNBJaMvr63ZuQ2MYxLdJoM//fTTamxsVF1dnfvHOZyWlpZtPBRgCuvrky64QLrqKuktbylMBN/Cv7Ox0lgZ0cJpFe6MeCIWVHtXSl19ebX2ZtTek1FFPKJ4pNDnPOhJ4UjAtWjJ5HLuxcCs+gpVRKNq78tofXufMq6S3XMvHHabVa1QMKgVbb1uCOmLLT16ek2X7BP1pnMuiNdXxgdVsBUr5Gu9iKu8G2q7uN2/vivlPn5Dd8ZVw82qTbitbS9s6HLHbYPXNnSntaq9170wGaqiHSh3xG6gPGL3wIq2nnTODSWzdmtdvUn3/lDQepx7bmh3LBJzC/OWHinbZSfAc25HmS3i5zZUuiS8F5BiobjaUxnX2qU3E1B1LKpXzKlWe2/OtXmbUZPQjOqEq3hrrIi5E+pezlM8YUO8g1vcMQZg2xG7Af/H7oFr2qVrOvTUqnat7LAdZAH1pTLqyXquBUsqZwO802qsjCoRi2jXmdWKzg27mWFeMODi+r7z612C3Nqcru3oU0dfRo+/1KbVtvbO5bWgsVK1s6MKhWyHeVRz65JudoqdIK9NhhWz3Wo1MXcCfmCB2sC+7ewsA0ZuRK98v/71r6uqqqr/7S0l0QFsg7e/XfrLX6Rvf1t6//snJIFuKmJRLW6q1gvNPepI5dQXyak7nXZbzKzMPJ3NqLYioc6ejAvctvDOZPOqjEddS5VXzK7V9Oqonl7TqReyOTf9uzoW0fz6CjVUxrSuM+XOrK9u69ODz29QVzqvxoqosp4F9uEr2LYU1O1jrG3dHnNq3NY0OxNfm4ypuStd+Fodfe7Muk0ft+p0SwTQ9w1TEbEbKI/YXaxos8Ha9y/v0SMvtCidlzuhPdfF24isiVpbT9/GrdxBVcUiaunOuDZsllW3+P1iS68bKm7xPGvvaOlxw7lj4Yjbbfbg861uK3kgkHCDv3ebXaN0Nq/23rTWdaRcuzbbYr6gocK1eyme5KaaDRg7xG6gfNbdtnv7mbVdWra2Q2s701o8rVItnX1a353VtOqoNnRlXGy3pmoBeapO2AntWteKxYrYplcXCs4s/2aXrnROzZ0pLV3b6QaIx8JBLW/ucp/XHmfr4Zdae9z6uTIW1uy6Ck2rfnn+2MA83sBdbuwsA8Y4iX7aaaf1v3366aeP4tMDGJZlgnt7pWRS+uQnpS9+Udpzzwl9wqydyq4zq9TZm1Iul1M+l1VXr5RyA0SDrhWKVZhbT/Rw0Hq3BZRMRlUdj7oKcIvDTVUJt0L/39oudaZySkeyml6dcBXgNkTULg8/36JUtlAlZ1vR3MDRRNRVxq1q69kssG8pqBcr8uxFQnWikEBv6ykk6+c3VrjH1FdEtKCxarMXC8BUQuwGyiN2FyvajLVFe6m1Vx19WddWJesFVFdZp3iosCV8dVshZlq89mwgeLaQH7C2bPmctXjJKajIxviadoPMqpJRdfakXXsYGzo226rYwkG3jXyHaVUuTgcCwf6YbAn0WbWFKvj+JAHVbMCYIHYD5bPutsrxlu60ZtQktaK1z80Iq0hE1JPzXDu1vKxvue38Drmk+LM2DDRkg0Njbq7XjI3J84Hr41l1ST2xqkNtvX3uhHfUys4DhSK0QrwOKBkNuaHhTVXRQfF6oKFmkXFSHNg6myE4KrZNZN26dZvdv2HDBve+8fD888/rrLPO0sKFC5VIJLTDDju4QSvpdHpcvh4w7trapBNPLGwhs6B+0EETnkB3h9GT0YutverNem7AmA0iCYciioQDbkq4LcJtqnciakE14P6NW1APBOUW2gcsbNDCxgp35rymIqam6pgaK+Na0JB0Z84tUW6Va/b2jjMq1VAR04L6hI7cdbp2n1XtEuErWnrd4tuOpciCuL1eeG59l2s1Y8l2C+oDK/Lm1ifUVFmoQLe2Lqs6+vRCc7eqk1GXQC+etQdA7AbKIXbbAjoeKgz1XtXSo56UVaT1uZ1da9p7XFLcep/b4zIbF+hViZBcC9RgQL3ZnDb0ZPXYynb956V2hYIBVcQiLpGezQeViIXcx1lS3CrOi61atjb0e3DPduuZXpyiAmB7sO4GfB673VLUU31FVLOqY5pdG9N+8+tcvLR1ths6Ggq4NWxnOqvO3pyea+5ySfVN42kxFlurl11mVLm5YzajbMn0Sk2virvHWkJ9bn3Srdtjm/RA39TANbVdu6HjG3eDD7U+B1Aw6kaGxUTWplKplKLRzdsyjIWlS5e6Ho7XXnutFi9erCeeeEJnn322uru7dcUVV4zL1wTGzb/+JZ10ktTaKn33uxPWumWg4lnmJ1a2aemaTrV09rqWKzawMxzxlM8EVF8RVmefbRMLuarzcCCrZDzs+q7Zxy5qrNCS6VUuYPek8y5QR2wgaTDo+rXa/daXLRmtUiwYcD3Kc56n/ebXa8+5tVrT0ad8d2bIanML4tb25cWWbuXzctvUbPt5fWWsvyLPHmtn2/Oe5xL5L1egF14EABj8b34oxG7AP7HbYnR7X1bdNlBUUlcqo3Qu6Nq0WG/zYCjg+pVb3Oz1cqqIhVQRDakv65q0qTIWcUNIbXFtj7f3h4IhhQJBzawNuvtm1Ma055x6F9+LsXRrQ7+3lmQHsG2I3YC/Y/e0yphLbD+7rlM1lXElQtKqjrRrj2qx2pLlNYmo1nb2KayAZtcn3dp24Fyv4rrd2r7Y+tgGie44vdBq2eJyOuv1X9swcDNUD/RNDVxTF9nXaO9Ju1YwNmfMbg+cXwZgFEn0b33rW/3/2L7//e+rsrKy/33WBuKuu+7SzjbZeBy8/vWvd5eiRYsWadmyZbr66qtJosNf7KTPhRdKr3yldOed0oIFk3IYFoj/t65TzzZ3a/m6TvVms67iOxYKKhoMqbIiqtl1CdcmJZW2jWaeZtUnFQ/bQtlTQ1XMVZnbx1swt97k9nHxWKGCvaXH+rH1udcp4YDcoFIbfGL9yefVJxUMBre46La/M9ZKpqsvp3AooCdWtqsiGtYrN6kuH9japcZVoL/cOx0AsRsop9htutNZFxdt4d2TyiokTys2dKsyHnEnmxsbK7TzjGqXELdhoOlszi3ULSB3pXJq6c663WNu0FgoqDn1FZpRHXMnzG1hvvPMGjfArL03O2i496YL7YG2lmQHMDqsu4HyiN22brUis6pExO2gtjVypqNP8xsq3VyT3mxeDcmwdp1V4xLW1bGQGzBqLVBtjW2tV7c0K6y1O6BV7RvfFwhoWlVcc+uD7qS7GRjHR7JD2xLxNl8sm/UUDge0Q9PLOT8Ao0yi25ATY2fCrrnmmkGtW6wCfcGCBe7+idLe3u6ml2+JVdjZpaijo2MCjgzYglisEMwvvVQKT16lli10rYe4Jc1z8rSmtce1XbHgbQWrM2qiqoxaIK7W9OqYnlrd6QaI1lfEXVVbJBJywXhdR68qIkHtM6/OLdALAdwW1FFXhf74S+16dl2HmrszaqyKuSni67pSaqiKb33RHZAbeNbdnXUDVB5f2app1XEtsPYxgYD7W2SXyljIdsm5ljEs3IHBiN1A+cRui9F2ErrCKsR6M64dS8xOQFv8tfdnclrT3qeFTZUu0d6TzWtmdUKz6hLqS2W1bH232rvzikcjWtJUoUQ84oaSzW+o0Jz6hNv2PXA791AL9pFWswHYdsRuoDxid182p7pk1M0Xa+lKu7X3tJqEejN59fVktbA+6arP7TG2hrZ9oxaD3VyyrpRbU29pVtim77Od4dYDfVtnldjJ9FnVcTdk3Ha92W0Ag434r8ny5cvd9WGHHaZf//rXqqur02R55plndNVVV221Cv3yyy/XZz/72Qk7LmBIv/+99Mgj0qc/LX3oQyXxJNnZ6baetNZ19mlWTULpTF75QEqtXspNA+9JeQrXRlyvVNtits/8ei2eXuWCaSqbdUPNfvtSu+oSEWVzng5e3Kgjdp7utqK1d6e1tjOlB5ZvcP3W7WOsCt16t0aKrw5GsOi23m4NlVG1rk25s/c2mHTZmg73YsI+zl5gWCV88cXBwMErAAqI3UD5xO7mroyL3bm8nUSWGipjCgWtciyvtu604tb/NBJ2leXexoqyxqpoobeqZz3SpXmNlVrbkdLqzrQSqbymVcVc8t0S6MUF9pYW7ADGH7EbKI/YbYVitntbSitWE3NFXy9u6NaydV1qqoy7hPq8uqRqKqIuwb5sbYc6+3KuBUygN9NfbDbc7u3h3retcdxeC9h8MftYu95ST3Vgqhr1YNE777xzUALdWrk8+uijarU+U6N04YUXuqTXli7WD32glStXutYuxx9/vOuLviUXXXSRq1gvXlasWDHqYwS2WV+fdO650pvfLD34oP1jKakn04JzcOO/M9suVh0LusX2EhssUhFxQd6qya2PuZ0dtyEmtu3MEu5dfVmt7+hzQ8pWtfW66vLAxq3iXamsW6Bb31ZLms+pTypqvVoD1ru80n3ekbDFvPVPt23ltYmI5tRVuK3nxQErIxlkZpXqdibeeqfb9XC9JYFyR+wG/B+7817excYdplW6nVmzauLuRHd1IuJ6odsJ8lDQU2tPRrm87RQNupPPf1u6To+80KqWrpzqkxE1VUU1uyamnWdU6YCF9W6I6FDDy+hxDkwuYjfg79jtdl5Pq9Qus6r1yoUNrq2pxfEFDRV61Q4NrhjN5obZGtqqz6OhkOoro9rQlXZFaBbX3c7raEjVibB2aKwYtPN6qOGg2xPHh/t8AF426n0t5513nvbYYw+dddZZLoH+mte8Rv/617+UTCb1+9//Xq997WtH/Lk+9rGP6fTTT9/iY6z/edGqVatcJfxBBx2k79pgiK2IxWLuAkw4O/nzzndKTz1ljQ2lD35wUoaQbWlrmVWfzalL6H9rOqSamCoSEb3U0usO05NVrFmleGzjoJKcAoGwbDaZ9UmzHuSRcNANEbUAbyVvxaR2ZSJS6LNaF3cJ9ng05Nq9zK1LukFlI+1Zbkl5a91iLAlgn9PO5BdfBIzkxcFot6QD5YrYDfg/dtclY25BbZdF0yrdi3iLxal0Vv9d3a58wFNvOqenVrepMh5VLue5KnNr/VIVj6irL62n1wXcyeyFTVWud2o65ykUKswpKRrYbq24gLeT0aPpqwpg+xG7AX/H7oE7r62gy3ZRW9y1nd9WdGaHOasu6XaUWSvTgVXrNhjUDNp5XT145/VwO7u3dVYJ7dmAcUii33zzzTrllFPc27/73e/0/PPPu2rxH//4x7r44ot1zz33jPhzNTU1uctIWAW6JdD33XdfXXfdda4nJFCyrryycEb8/vulPfdUqbFgaotmC+Zp64FurVOq4tpzTq2iNu4gENTa9j5t6EmrL5VRT1aub2pvJqfGZMQNEn12XbcSkWB/dbkFXatsX9naq65UWmvbpWQspHl1Ff3J89EuvIuJ9GI/uIEvAkby4sDel7Oz+JGQm3JuZ/FJAGAqInYD/o/dtjPr4CVN+u/KNnfbTnBbtdp/V6eUygbUkU6rrTuryt6wZlZbKA+opy+taCSs6VVR2UvngOe56vXmrpRm1SZcfLSFe3HOSHEn6MBFPyejgclB7Ab8H7sHzjXp6EmrIhZSdTSs2oqwcl7A7fa2dbm1MrUk+cC1rQ0G3Za2LKNNhlv8t+KzgV+bE+bAGCXRN2zYoBkzZri3//jHP7q2KjvuuKPOPPNMffOb39R4sAS6VbjPnz/f9UFfv359//uKxwJMuvZ26bHHpNe8phDMLWFcUaikLjXFBPTzzZ2ub+rCxgoXmGuS1gPdtoWH1FTVq+Xru9WVzukfy9aprTuleDSsXWZUaW5VTE1VcdfDbWB1ubV/Wd/Zq4ZkTM09Kc2LV7hFfHFRvi0GvggYKsDXBYZ/cWCPscT/srWdliNwyYIZNQmq0THlELsB/8duKyDZc26tGwS6pqNXq9t61dbb6qrPp1dFlG7LKRoKqipusS/r4l0iHFJPOqtsPuCGjNqQ7/0W1Ou59d3q6Esr6xV6n3alurV4Y7wdiP7owOQhdgP+j91FNqdkVUefsllP4XBAe8yp6d/1PTBxPTDxPVHt1di9DYzcqP8VTp8+XU8++aRmzpyp22+/XVdffbW7v6enR6GQlbCOvTvuuMMNE7XLnDlzBr2PHscoCffdJ514opTN2uRbqbJSpayYmJaqlMl3anlzt1p70lrfacnzmDsbbn3SbdhJ88pWJaNBVwFnATYWlubUJbXv/Hj/YruY2LY2MfUVMdVXxpRa5WlGTdwNPxuroWSjDfD2YsSOoSeVdVvl7Ew/A9IwFRG7Af/H7mL8tthmSXRLntsJ7Upr1ZKS2w2Wl9wgbut9bj3TbWv4+q4+d9J7p5nVeqGlxyXQbQEfD4e3WuFGf3Rg8hC7gfKI3SYatpPZcVXGwursy7h186YJ9E0LxmoS4W1qyzJanDAHRm7UPVHOOOMMnXDCCdp9993dP/QjjzzS3f/vf/9bO++8s8aD9U0vbjPd9AJMqnxeuvxy6eCDbVuE9M9/WjN+3/xQLBA3VcaUzeVdsntDV5/r0bauvdcFUxteMrs2qXg0opdae7Wms0+pjFx1erG6vJjYXtHSqzXtfa7/andf1i3QbcjoWJ41H8kw0YHs+GZUJ9zJAOs1t2nfV2CqIHYD5RO7Le5avG3uTqu9J61ZdQnNro5p8bQK7TwtqQX1Ce04LamKaFjru1JukLiCITeb5DVLpmmvudU6eIdGLZ5eudUKN4aMAZOH2A2UT+xORiOqSRbam9qa1Hqi2/rZ1tEW183AdbVdt/dmXcHYrNrCQNLxarHCCXNg5EadTbr00ktdAn3FihWulUtxcKdVoV944YWj/XSAv330o4UBJp/8pHTJJVLEXxOsLRDHIkG3lcy2ff/ruWY99mKrC/DJWFgza5LaaUal1nWk1Nzdp46+nOY3JvoT2FaxNjCxva7Deq1G3PTwRU2V7oy7vWAYq7Pm2xLgt3WwClBOiN2A/2N3sULNWrHZye9XzK7R0+s63dDQrJfU4y+1u8flrbd5MKSeTF6V0bD2mFendR0WwzPae36DpMr+zxcMBLcYHxkyBkweYjfg/9g91Jq0vSfjhn5vuhNssirCWS8DIxfwplA5d0dHh2pqatTe3q7q6sK0Y2CbdHcX+q7ZFrIXX5QOP9y3T2RxaJgN4HyptcdtBd9xWpX6MjnNa7AWKHnd/WyzOnoyau1Na/eZNa7qzSrYLQFv72/uSrtF+0harGwPhp6g3BGneE4wjnweu4vx2gaTWV9V2xaeyeVdH/RkNKR7n92glq6U2znWVGUnwyNu27i1fLHdYVZ9vrDJH9veAT8hdvOcYBz5PHYPZbih3QzzBko/do+4ncsb3/hG98mLvvSlL6mtrW3Q4JNdd911NMcM+E8qJZ13nrTvvjYIQFq82PeBvHjm2RLm+86v18KGSqU2tj6xAaN2BjwaDGjJtArtNqNa8xoKCXRLnNtWM9sqbgNF59Yn3OcZqpLNkt/2omBVW4+73tZzd8WKuPHe0gaUC2I3UD6xu1ihtqCxQrOq42qoiGjnGdWu73l3Oqdp1XHVV0YVDAUUi4YVkKcdp1f2t2+x2SYASh+xGyif2D2U4Vql0UINKKN2Ln/605+Usj9kG1122WWuN3ptba27nc1mtWzZsvE5SqAU2O/3O98pPfmkdMUVUiKhcjBwq7Ylt2uT0f6t3Xbb+rX1ZfNa05HSnPqkFjZWFRby3stbzawi3RLbw2HiNzA5iN2Y8soodhdbmtlJ7OpkVAsaCwtvi9s96Yx2aKrUCxs63YDR6nhYmbynHWdUaddZhdfqAPyB2I0pr4xi91CGa5VGCzWgjJLom1aOTqEuMID0059K7363NHeuTdGV9tprSjwrhWR6SHvOqdWq9l63ddydKe/RqHqTM/EbmBzEbkxpZRa7ixVqljBPZz13bWoShRgcCGQ1r75SOS+gvnROiVjYDdcG4C/EbkxpZRa7AUzxwaLAlFRTUzgbbsNMrCdbmb5gf765W8vWdCgSCrrt4damxdq6WHsX66lqw0ftDLkt2BsromrrSbkKuOICvvh5rPJ84KAyJn4DACZcmcXuYoWaWdVe7KWacvG4uTtduB0IaFFjpdshVozBzBMBAPhGmcVuAOUlOJoX7pv2H6YfMcraffdJH/iAZYWtOaH0gx+UdSC3XuUPvtCipWs6tLYzpfaelKLhwJD92tp7s27Bns7JXdvtTVu3WL90u7bbo+3vNlY91IGpjtiNKWcKxO6Bu7vs2k5o23DwWDio9V19bjD4zJpE/+yQoeIygNJF7MaUMwViN4Ap2M7l9NNPVywWc7f7+vp0zjnnqGLjH7eB/dIBX8vnpS9/Wfr0p6X995dsoO7G3v/lbG1nnzZ0pZTLS8+s61Q0XK1kNDJkv7YttWcZ8n2B6JCfZzj0UAfGBrEbU8YUit2b7u6yHWHPb+jWsrWdsnKXeCTkdo4Vq9ZpqQb4C7EbU8YUit0AplgS/bTTTht0+5RTTtnsMe9617vG5qiAybJqlf0iS3/7m3TRRdKll0qRLVdNlw2vsPCujEW0oTulubXJYSvGt9SeZSxat7DgB8YGsRtTwhSL3QN7o6cyefWkMu4SDwW0ZEa1Upmc1nT09rdVi4dD2x2XAUwcYjemhCkWuwGUhxG/ir7uuuvG90iAUnDTTYUp4HfcIR1xhKaS6dVxzW+oUG8mp9l1CS2ZXjVsy6biAn5g3/ORvG+k6KEOjA1iN6aEKRa7B/ZGX7am0IZtxYZe5bycsnlP02vi6svm1dGbdUnzHRortjsuA5g4xG5MCVMsdgMoDwFvCjUb7ujoUE1Njdrb21VdXT3Zh4NSYa2I7rxTev3rpVxOamuTGho01ZTS4LFSOhZgIhGneE4wQsRuNzfk7mea9cRLbW4weC7vaYfplTpkcZOq4hFNq4676nObRzKrNsmvFjBOiN08JxghYjcAn8du9nNialu2rDD9266XL5emT5+SCfSBlW0j7Vs+VY4FAFBiiN0v79qStKErLWuGPqsuoWQk7IaCh0JB2rcAAEoHsRtAGbDX3sDUYxswrEXRPvtIvb3SvfcWEuiY5B+Lp9butKuus+sptFEGALA1xO5BbJfWPvPqtMfsGtVXxBQJBt0J6Ll1hfYtVoFu17RvAQBMGmI3gDJCJTqmpm9/W/rwh6Uzz5S+9S2pomKyjwiSa+HyzPou5fOe6+Nqi/9i31cAwBRH7N5s19bCpkodv99cFzttoOicuqQWNFYoaAl1dnMBACYbsRtAGSGJjqmlq0uqrJROOUWaNUt629sm+4gwgPVAtwR6U1XMbUO32yQBAGCKI3ZvMZHeUBV3FwAASgaxG0AZop0LpoZ8Xvryl6UddpBWrpTq6kigl2p/12CAPq4AAGI3AAB+w7obQBmjEh3lb/Vq6V3vkv76V+nCC6Vp0yb7iDAM69tqLVysAt0S6vRxBYApitgNAIC/ELsBlDmS6Chvf/+7dMIJUjgs3XGHdMQRk31E2Mq2dOuBTgsXAJjCiN0AAPgLsRvAFEA7F5S3xkbpkEOkxx4jgQ4AgB8QuwEA8BdiN4ApgCQ6ys+yZdKpp0p9fdLuu0u/+pXU1DTZRwUAAIZD7AYAwF+I3QCmGJLoKB+eJ11/vbTvvtL990urVk32EQEAgC0hdgMA4C/EbgBTFEl0lIeODunkk6Uzzij0QH/oIWnRosk+KgAAMBxi93bzPE+t3Wmtautx13YbAIBxQ+wGMIUxWBTlM8jkD3+QbrpJOvHEyT4aAACwNcTu7dbWk9Ez67uUz3sKBgNa3FTpBnQDADAuiN0ApjAq0eFf+bx0yy2F7WRveYv07LMk0AEAKGXE7jHVm8m6BHpTVcxd220AAMYUsRsAHJLo8KfVq6Wjj5be+tZC65biRHAAAFCaiN1jLhEJuwr09Z0pd223AQAYM8RuAOjHK234z223SaedJoVC0p//LO2332QfEQAA2BJi97ioTUZcCxerQLcEut0GAGBMELsBYBAq0eEvf/yj9MY3SvvvL/3nP9KRR072EQEAgC0hdo+bQCDgEueWQLdEuvVIZ7goAGC7EbsBwP9J9FQqpb322sstGh599NHJPhxMlM7OwvXrXif95CfS738vNTXx/AOADxC7pyhi94QOF13R0uuu7TYAbC9i9xRF7AaA8kmiX3DBBZo1a9ZkHwYmig0N/dGPpPnzpQcflCIR6eSTrfSKnwEA+ASxe4ohdk8ohosCGA/E7imG2A0A5ZVEv+222/TnP/9ZV1xxxWQfCiZCR4d06qmF/ufHHivtvDPPOwD4DLF7iiF2TziGiwIYa8TuKYbYDQDlNVh07dq1Ovvss/Xb3/5WyWRysg8H4+2JJwqJ8/XrpRtvlE46ieccAHyG2D3FELsnBcNFAYwlYvcUQ+wGgPJKotuApNNPP13nnHOO9ttvPz3//PMj7uNml6IOO8MKf5g2Tdp9d+nrX5cWLZrsowEAjBKxewoidk8KmxNUVxFVnaKTcwAAygaxewoidgOAP9q5XHjhhe6F/5YuS5cu1VVXXaXOzk5ddNFFo/r8l19+uWpqavovc+fOHbfvBWNg9epCxfnatYVgfsstJNABoMQQuzEIsRsASh6xG4MQuwFgmwQ8O908SdavX68NGzZs8TGLFi3SCSecoN/97ncuqV6Uy+UUCoV08skn64YbbhhxJbol0tvb21VdXT2G3wm22223FXqfh0KF5PkrX8mTCmDKsThlJ31LOU4Ru9GP2A0AxG7W3f5C7AYAbeu6e1KT6CP14osvDmrFsmrVKh199NH65S9/qQMOOEBz5swpm+TElGMnOT75SenKK6U3vEG6/vpCFToATEHlFKeI3WWM2A0A/Yjd5f2clA1iNwBsd5zyRU/0efPmDbpdWVnprnfYYYcRJ9BRopYula69tpBEP/dcKTipHYYAAGOE2F3GiN0AUJaI3WWM2A0A280XSXSUGdv8cOut0hvfKO25p/TCC1JDw2QfFQAAGA6xGwAAfyF2A8CY8mXZ74IFC9zk8L322muyDwWjZW15Tj1VOu446be/LdxHAh0Ayh6x28eI3QAwJRG7fYzYDQBjjkp0TJz775dOPNGm0kk33igdfzzPPgAApYzYDQCAvxC7AWBckETHxHjySenVr5b23lv685+toT3PPAAApYzYDQCAvxC7AWDc+LKdC3y2jczssov0ox9Jd99NAh0AgFJG7AYAwF+I3QAw7kiiY/z86U/SkiXSL38pBQKFVi7RKM84AAClitgNAIC/ELsBYEKQRMfYS6elj39cev3rpX32kV7zGp5lAABKGbEbAAB/IXYDwISiJzrG1qpV0lveIv3nP9LXviadd54U5FwNAAAli9gNAIC/ELsBYMKRRMfYamiQFi6Urr1W2ndfnl0AAEodsRsAAH8hdgPAhKNEGNuvs1M688xC9XksJt18Mwl0AABKGbEbAAB/IXYDwKQiiY7t8+CD0t57FxLnzz/PswkAQKkjdgMA4C/EbgCYdCTRsW3yeemKK6QDD5Tq6qRHHin0QgcAAKWJ2A0AgL8QuwGgZJBEx7ZZv1768pelj3xEuuceafFinkkAAEoZsRsAAH8hdgNAyWCwKEbnL3+R9tlHmj5dWrZMqq/nGQQAoJQRuwEA8BdiNwCUHCrRMTLptPTxj0uve510zTWF+0igAwBQuojdAAD4C7EbAEoWlejYumeekU48UXrsMelrX5POO49nDQCAUkbsBgDAX4jdAFDSSKJjy1pbpf33lxobpX/9S9p3X54xAABKGbEbAAB/IXYDQMkjiY6hdXVJiYRUVyddf710+OFSVRXPFgAApYrYDQCAvxC7AcA36ImOzT34oLT33tJXvlK4feyxJNABAChlxG4AAPyF2A0AvkISHS/L5ws9zw86SKqtlY4/nmcHAIBSRuwGAMBfiN0A4Esk0VHQ3S0dc4x0/vmFwaH33CMtXsyzAwBAqSJ2AwDgL8RuAPAteqKjIJmU5s6V/vQn6aijeFYAACh1xG4AAPy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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Scatter plots: estimated CATE vs true CATE for all learners\n", + "\n", + "def _scatter_all(cate_map, truth, title, xlabel):\n", + " names = list(cate_map.keys())\n", + " n_cols = 3\n", + " n_rows = int(np.ceil(len(names) / n_cols))\n", + " fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows))\n", + " axes = np.atleast_1d(axes).ravel()\n", + "\n", + " for ax, name in zip(axes, names):\n", + " cate_hat = cate_map[name]\n", + " ax.scatter(truth, cate_hat, alpha=0.15, s=5)\n", + " ax.plot(scatter_lims, scatter_lims, 'r--', lw=1)\n", + " ax.set_xlim(scatter_lims)\n", + " ax.set_ylim(scatter_lims)\n", + " ax.set_xlabel(xlabel)\n", + " ax.set_ylabel('Estimated CATE')\n", + " ax.set_title(name)\n", + "\n", + " for ax in axes[len(names):]:\n", + " ax.axis('off')\n", + "\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "_scatter_all(total_results, true_cate_total,\n", + " 'Estimated vs true total CATE (RMTL)',\n", + " 'True CATE (total)')\n", + "_scatter_all(direct_results, true_cate_direct,\n", + " 'Estimated vs true separable direct CATE (RMTL)',\n", + " 'True CATE (sep direct)')\n", + "_scatter_all(indirect_results, true_cate_indirect,\n", + " 'Estimated vs true separable indirect CATE (RMTL)',\n", + " 'True CATE (sep indirect)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9048a6e0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Boxplots of |bias| and estimated HTE distributions for all learners\n", + "\n", + "def _boxplot_bias(cate_map, truth, title):\n", + " names = list(cate_map.keys())\n", + " biases = [np.abs(cate_map[k] - truth) for k in names]\n", + "\n", + " plt.figure(figsize=(14, 5))\n", + " plt.boxplot(biases, showmeans=True)\n", + " plt.xlim(*violin_xlim)\n", + " plt.ylim(0.0, violin_ymax)\n", + " plt.xticks(range(1, len(names) + 1), names, rotation=45, ha='right')\n", + " plt.ylabel('|Bias| distribution')\n", + " plt.title(title)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "def _boxplot_estimates(cate_map, title):\n", + " names = list(cate_map.keys())\n", + " estimates = [cate_map[k] for k in names]\n", + "\n", + " plt.figure(figsize=(14, 5))\n", + " plt.boxplot(estimates, showmeans=True)\n", + " plt.xlim(*violin_xlim)\n", + " plt.ylim(estimate_ymin, estimate_ymax)\n", + " plt.xticks(range(1, len(names) + 1), names, rotation=45, ha='right')\n", + " plt.ylabel('Estimated CATE distribution')\n", + " plt.title(title)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "_boxplot_bias(total_results, true_cate_total,\n", + " 'Absolute bias distribution — Competing risks total effect (RMTL)')\n", + "_boxplot_bias(direct_results, true_cate_direct,\n", + " 'Absolute bias distribution — Competing risks separable direct effect')\n", + "_boxplot_bias(indirect_results, true_cate_indirect,\n", + " 'Absolute bias distribution — Competing risks separable indirect effect')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2ad3d445", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_boxplot_estimates(total_results,\n", + " 'Estimated CATE distribution — Competing risks total effect (RMTL)')\n", + "_boxplot_estimates(direct_results,\n", + " 'Estimated CATE distribution — Competing risks separable direct effect')\n", + "_boxplot_estimates(indirect_results,\n", + " 'Estimated CATE distribution — Competing risks separable indirect effect')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Solutions/Censored Outcomes - Bone Marrow Transplant.ipynb b/notebooks/Solutions/Censored Outcomes - Bone Marrow Transplant.ipynb new file mode 100644 index 000000000..398ae3fbd --- /dev/null +++ b/notebooks/Solutions/Censored Outcomes - Bone Marrow Transplant.ipynb @@ -0,0 +1,2275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "title", + "metadata": {}, + "source": [ + "# Censored Outcomes — Bone Marrow Transplant Case Study\n", + "\n", + "This notebook demonstrates heterogeneous treatment effect (HTE) estimation with censored outcomes\n", + "on a real dataset from the Center for International Blood and Marrow Transplant Research (CIBMTR).\n", + "\n", + "## Clinical Background\n", + "\n", + "Allogeneic hematopoietic cell transplantation (HCT) is a therapy for acute myeloid leukemia (AML)\n", + "and myelodysplastic syndromes (MDS). Before transplantation, the host receives a conditioning\n", + "regimen to eliminate hematopoietic stem cells. Conventional myeloablative conditioning (MAC) can\n", + "be toxic, so reduced intensity conditioning (RIC) is sometimes used as an alternative.\n", + "\n", + "- **MAC (control, A=0)**: `strataf` in {1, 2} \n", + "- **RIC (treatment, A=1)**: `strataf` in {3, 4}\n", + "\n", + "RIC may reduce non-relapse mortality (NRM, competing risk) but be less effective at preventing\n", + "relapse. We study three causal quantities at **τ = 4 years**:\n", + "\n", + "1. **Relapse — total effect** (RMTL, competing risks)\n", + "2. **Relapse — separable direct & indirect effects** (RMTL, competing risks)\n", + "3. **Mortality (NRM)** — single-cause (RMST)\n", + "4. **Composite** relapse/mortality (RMST, single-cause framing)\n", + "\n", + "## Data Availability\n", + "\n", + "The dataset (`lk1602public.sas7bdat`) is **publicly available** through CIBMTR's research data\n", + "program. It was previously published by Bejanyan et al. (2021) *Myeloablative versus Reduced-\n", + "Intensity Conditioning with Matched-Unrelated Donors for Myeloid Malignancies* and is available\n", + "for secondary analysis at:\n", + "\n", + "> **CIBMTR data portal**: https://cibmtr.org/Manuscript/a020h00001F1sihAAB/P-5370\n", + "\n", + "Access requires registration and agreement to CIBMTR's Terms of Use for Publicly Available\n", + "Research Datasets. This analysis uses de-identified data; institutional review board approval\n", + "was not required.\n", + "\n", + "## Python Implementations Used\n", + "\n", + "| R (original) | Python (this notebook) |\n", + "|---|---|\n", + "| `two_learner` | `CompetingRisksTLearner`, `SurvivalTLearner`, `TLearner` |\n", + "| `iptw_learner` | `IPTWLearner` |\n", + "| `ra_learner` | `RALearner` |\n", + "| `u_learner` | `ULearner` |\n", + "| `aipcw_cut_rmtlj` | `aipcw_cut_rmtlj` |\n", + "| `aipcw_cut_rmtlj_sep_direct_astar1` | `aipcw_cut_rmtlj_sep_direct_astar1` |\n", + "| `aipcw_cut_rmst` | `aipcw_cut_rmst` |\n", + "| `survSuperLearner` | default `RandomSurvivalForest` |\n", + "| `SuperLearner` (propensity/outcome) | default `RandomForestClassifier` / `RandomForestRegressor` |" + ] + }, + { + "cell_type": "markdown", + "id": "section1_header", + "metadata": {}, + "source": [ + "## Section 1: Dataset & Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "imports", + "metadata": {}, + "outputs": [], + "source": [ + "import importlib\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "\n", + "from econml.metalearners._censor_metalearners import (\n", + " SurvivalTLearner,\n", + " SurvivalSLearner,\n", + " CompetingRisksTLearner,\n", + " CompetingRisksSLearner,\n", + " SeparableDirectAstar1TLearner,\n", + " SeparableIndirectAstar1TLearner,\n", + " SeparableDirectAstar1SLearner,\n", + " SeparableIndirectAstar1SLearner,\n", + " TLearner,\n", + " SLearner,\n", + " XLearner,\n", + " IPTWLearner,\n", + " AIPTWLearner,\n", + " MCLearner,\n", + " MCEALearner,\n", + " ULearner,\n", + " RALearner,\n", + " RLearner,\n", + " IFLearner,\n", + ")\n", + "import econml.grf._causal_survival_forest as _causal_survival_forest\n", + "_causal_survival_forest = importlib.reload(_causal_survival_forest)\n", + "CausalSurvivalForest = _causal_survival_forest.CausalSurvivalForest\n", + "from econml.grf import GRFCausalForest as CausalForest\n", + "from econml.censor import (\n", + " fit_nuisance_survival,\n", + " ipcw_cut_rmst, bj_cut_rmst, aipcw_cut_rmst,\n", + " ipcw_cut_rmtlj, bj_cut_rmtlj, aipcw_cut_rmtlj,\n", + " aipcw_cut_rmtlj_sep_direct_astar1, aipcw_cut_rmtlj_sep_indirect_astar1,\n", + " uif_diff_rmst,\n", + " uif_diff_rmtlj, uif_diff_rmtlj_sep_direct_astar1, uif_diff_rmtlj_sep_indirect_astar1,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "load_data", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw dataset: 4,387 rows, 25 columns\n", + "After filtering: n = 4,284\n", + "Treatment balance: A=0 (MAC): 2,597, A=1 (RIC): 1,687\n" + ] + } + ], + "source": [ + "# Load SAS dataset\n", + "df = pd.read_sas('lk1602public.sas7bdat')\n", + "print(f'Raw dataset: {df.shape[0]:,} rows, {df.shape[1]} columns')\n", + "\n", + "tau = 4 * 365.25 # 1461 days\n", + "clip_hte = lambda y: np.clip(np.asarray(y, dtype=float).ravel(), -2 * tau, 2 * tau)\n", + "\n", + "# Treatment: MAC (strataf in {1,2}) → A=0; RIC (strataf in {3,4}) → A=1\n", + "df['A'] = df['strataf'].isin([3, 4]).astype(float)\n", + "\n", + "# Risk level covariate: strataf in {1,3} → low/intermediate; {2,4} → high/very high\n", + "df['risk_level'] = df['strataf'].isin([2, 4]).astype(float)\n", + "\n", + "# Filter missing / coded-missing values\n", + "df = df[df['drsex'] != 99]\n", + "df = df[df['atggrp'] != 99]\n", + "df = df[df['kps'] != 99]\n", + "\n", + "# Confounder list (14 variables)\n", + "cat_confounders = ['drsex', 'disease', 'distatus', 'amstatprgp', 'kps', 'wbcdxgp', 'atggrp',\n", + " 'gvhdgpf', 'drcmvprf', 'donorgp_final', 'agegpff', 'graftype', 'risk_level']\n", + "confounders = ['yeartx'] + cat_confounders\n", + "\n", + "df = df.reset_index(drop=True)\n", + "n = len(df)\n", + "print(f'After filtering: n = {n:,}')\n", + "print(f'Treatment balance: A=0 (MAC): {(df.A==0).sum():,}, A=1 (RIC): {(df.A==1).sum():,}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "derive_outcomes", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature matrix X: (4284, 14)\n", + "Competing events — censored: 1439, relapse: 1778, NRM: 1067\n", + "Mortality events — censored: 3217, NRM: 1067\n", + "Composite events — censored: 1439, event: 2845\n", + "tau = 1461.0 days (4 years)\n" + ] + } + ], + "source": [ + "# Build design matrix X (encode categoricals as dummies, drop first for identifiability)\n", + "X_df = pd.get_dummies(df[confounders].astype(float), drop_first=True)\n", + "X = X_df.values.astype(float)\n", + "T = df['A'].values\n", + "\n", + "print(f'Feature matrix X: {X.shape}')\n", + "\n", + "# -------------------------------------------------------------------\n", + "# Competing risks outcome (relapse as cause 1, NRM as cause 2)\n", + "# time = min(time-to-relapse, time-to-death) × 30.4167 days/month\n", + "# event = 0 censored, 1 relapse, 2 NRM\n", + "# -------------------------------------------------------------------\n", + "time_competing = np.round(np.fmin(df['intxrel'].fillna(df['intxsurv']), df['intxsurv']) * 30.4167).astype(float)\n", + "rel_event = df['rel'].fillna(0).values.astype(float)\n", + "trm_event = df['trm'].fillna(0).values.astype(float)\n", + "event_competing = (rel_event + 2 * trm_event).astype(float) # 0/1/2\n", + "\n", + "# Mortality outcome (single cause)\n", + "time_mortality = np.round(df['intxsurv'] * 30.4167).astype(float)\n", + "event_mortality = trm_event.copy()\n", + "\n", + "# Composite relapse-or-mortality outcome (single cause framing)\n", + "time_composite = time_competing.copy() # same time as competing risks\n", + "event_composite = np.clip(rel_event + trm_event, 0, 1).astype(float)\n", + "\n", + "print(f'Competing events — censored: {(event_competing==0).sum()}, relapse: {(event_competing==1).sum()}, NRM: {(event_competing==2).sum()}')\n", + "print(f'Mortality events — censored: {(event_mortality==0).sum()}, NRM: {(event_mortality==1).sum()}')\n", + "print(f'Composite events — censored: {(event_composite==0).sum()}, event: {(event_composite==1).sum()}')\n", + "print(f'tau = {tau:.1f} days ({tau/365.25:.0f} years)')" + ] + }, + { + "cell_type": "markdown", + "id": "section2_header", + "metadata": {}, + "source": [ + "## Section 2: Shared Nuisance Estimation (Competing Risks)\n", + "\n", + "We fit censoring, event, cause-specific, and competing event survival models once and reuse them\n", + "across Sections 3 and 4. The notebook relies on the package defaults: `RandomSurvivalForest` for censored nuisance functions and direct survival learners, and random forests for binary and continuous pseudo-outcome learners." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "nuisance_competing", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting nuisance models for competing risks...\n", + "Done. Time grid: 1493 points, range [1, 3285] days\n" + ] + } + ], + "source": [ + "print('Fitting nuisance models for competing risks...')\n", + "nuis = fit_nuisance_survival(\n", + " time_competing, event_competing, T, X,\n", + " cause=1,\n", + " cv=2,\n", + ")\n", + "tg = nuis.time_grid\n", + "print(f'Done. Time grid: {len(tg)} points, range [{tg.min():.0f}, {tg.max():.0f}] days')\n" + ] + }, + { + "cell_type": "markdown", + "id": "section3_header", + "metadata": {}, + "source": [ + "## Section 3: Relapse — Total Effect (Competing Risks RMTL)\n", + "\n", + "Maps to `relapse_total_hte.R`. The estimand is the Restricted Mean Time Lost (RMTL) for cause 1\n", + "(relapse) at τ = 4 years.\n", + "\n", + "Three pseudo-outcome transforms are available:\n", + "- **IPCW** (`Y_ipcw_total`): singly robust, requires correct censoring model\n", + "- **BJ** (`Y_bj_total`): singly robust, requires correct event model \n", + "- **AIPCW** (`Y_aipcw_total`): doubly robust — used for learners below\n", + "\n", + "All six transforms from Sections 3–4 (`Y_ipcw_total`, `Y_bj_total`, `Y_bj_sep_direct_astar1`,\n", + "`Y_bj_sep_indirect_astar1`, `Y_aipcw_sep_direct_astar1`, `Y_aipcw_sep_indirect_astar1`) can\n", + "replace `Y_aipcw_total` as the input to learners, depending on the quantity of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cut_total", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Y_ipcw_total: mean=494.81, std=623.85, finite=True\n", + "Y_bj_total: mean=494.48, std=617.65, finite=True\n", + "Y_aipcw_total: mean=494.80, std=622.87, finite=True\n" + ] + } + ], + "source": [ + "# IPCW CUT (singly robust, censoring model)\n", + "Y_ipcw_total = ipcw_cut_rmtlj(\n", + " T, time_competing, event_competing, tau,\n", + " nuis.G_a0, nuis.G_a1,\n", + " time_grid=tg,\n", + ")\n", + "\n", + "# BJ CUT (singly robust, event model)\n", + "Y_bj_total = bj_cut_rmtlj(\n", + " T, time_competing, event_competing, tau,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " time_grid=tg,\n", + ")\n", + "\n", + "# AIPCW CUT (doubly robust) — primary pseudo-outcome for learners\n", + "Y_aipcw_total = aipcw_cut_rmtlj(\n", + " T, time_competing, event_competing, tau,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " time_grid=tg,\n", + ")\n", + "\n", + "print(f'Y_ipcw_total: mean={Y_ipcw_total.mean():.2f}, std={Y_ipcw_total.std():.2f}, finite={np.isfinite(Y_ipcw_total).all()}')\n", + "print(f'Y_bj_total: mean={Y_bj_total.mean():.2f}, std={Y_bj_total.std():.2f}, finite={np.isfinite(Y_bj_total).all()}')\n", + "print(f'Y_aipcw_total: mean={Y_aipcw_total.mean():.2f}, std={Y_aipcw_total.std():.2f}, finite={np.isfinite(Y_aipcw_total).all()}')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "learners_total", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Relapse total effect ATEs (positive = RIC increases RMTL for relapse = harmful):\n", + " CompetingRisksSLearner : ATE = 154.0064\n", + " CompetingRisksTLearner : ATE = 151.8714\n", + " AIPCW + TLearner : ATE = 145.4610\n", + " AIPCW + SLearner : ATE = 153.6940\n", + " AIPCW + CausalForest : ATE = 170.4133\n", + " IFLearner (total) : ATE = 277.5239\n", + " AIPCW + XLearner : ATE = 159.8414\n", + " AIPCW + AIPTWLearner : ATE = 165.4247\n", + " AIPCW + RLearner : ATE = 153.2400\n", + " AIPCW + IPTWLearner : ATE = 178.7623\n", + " AIPCW + MCLearner : ATE = 16.7619\n", + " AIPCW + MCEALearner : ATE = 149.0004\n", + " AIPCW + ULearner : ATE = 130.2736\n", + " AIPCW + RALearner : ATE = 146.9260\n" + ] + } + ], + "source": [ + "results_total = {}\n", + "\n", + "# Direct competing-risk outcome object\n", + "competing_obj = np.array(\n", + " [(int(e), t) for e, t in zip(event_competing, time_competing)],\n", + " dtype=[('event', int), ('time', float)],\n", + ")\n", + "\n", + "cr_slearner = CompetingRisksSLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "cr_slearner.fit(competing_obj, T, X=X)\n", + "results_total['CompetingRisksSLearner'] = clip_hte(cr_slearner.effect(X))\n", + "\n", + "cr_tlearner = CompetingRisksTLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "cr_tlearner.fit(competing_obj, T, X=X)\n", + "results_total['CompetingRisksTLearner'] = clip_hte(cr_tlearner.effect(X))\n", + "\n", + "tl = TLearner(cv=2)\n", + "tl.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + TLearner'] = clip_hte(tl.effect(X))\n", + "\n", + "sl = SLearner(cv=2)\n", + "sl.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + SLearner'] = clip_hte(sl.effect(X))\n", + "\n", + "cf = CausalForest(num_trees=200, seed=0)\n", + "cf.fit(X, Y_aipcw_total, T)\n", + "results_total['AIPCW + CausalForest'] = clip_hte(cf.effect(X))\n", + "\n", + "ps = nuis.ps\n", + "bw = nuis.iptw\n", + "tilt = nuis.naive\n", + "Y_uif_diff_total = uif_diff_rmtlj(\n", + " T, time_competing, event_competing, tau,\n", + " bw, tilt,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " cause=1, time_grid=tg,\n", + ")\n", + "if_total = IFLearner(cv=2)\n", + "if_total.fit(Y_uif_diff_total, X=X)\n", + "results_total['IFLearner (total)'] = clip_hte(if_total.effect(X))\n", + "\n", + "xl = XLearner(cv=2)\n", + "xl.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + XLearner'] = clip_hte(xl.effect(X))\n", + "\n", + "aiptw = AIPTWLearner(cv=2)\n", + "aiptw.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + AIPTWLearner'] = clip_hte(aiptw.effect(X))\n", + "\n", + "r_est = RLearner(cv=2)\n", + "r_est.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + RLearner'] = clip_hte(r_est.effect(X))\n", + "\n", + "iptw = IPTWLearner(cv=2)\n", + "iptw.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + IPTWLearner'] = clip_hte(iptw.effect(X))\n", + "\n", + "mc = MCLearner(cv=2)\n", + "mc.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + MCLearner'] = clip_hte(mc.effect(X))\n", + "\n", + "mcea = MCEALearner(cv=2)\n", + "mcea.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + MCEALearner'] = clip_hte(mcea.effect(X))\n", + "\n", + "u = ULearner(cv=2)\n", + "u.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + ULearner'] = clip_hte(u.effect(X))\n", + "\n", + "ra = RALearner(cv=2)\n", + "ra.fit(Y_aipcw_total, T, X=X)\n", + "results_total['AIPCW + RALearner'] = clip_hte(ra.effect(X))\n", + "\n", + "print('Relapse total effect ATEs (positive = RIC increases RMTL for relapse = harmful):')\n", + "for name, cate in results_total.items():\n", + " print(f' {name:35s}: ATE = {cate.mean():.4f}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "section4_header", + "metadata": {}, + "source": [ + "## Section 4: Relapse — Separable Direct & Indirect Effects\n", + "\n", + "Maps to `relapse_separable_direct_astar1_hte.R` and `relapse_separable_indirect_astar1_hte.R`.\n", + "\n", + "The separable direct effect isolates RIC's pathway through stem cell elimination (relapse-specific),\n", + "while the indirect effect captures the pathway through NRM reduction.\n", + "\n", + "**Note**: IPCW CUT is **not available** for separable effects — the censoring weighting cannot\n", + "be factored along the cause-specific pathway. Available transforms:\n", + "- **BJ separable** (`Y_bj_sep_direct_astar1`, `Y_bj_sep_indirect_astar1`): G=1 (no censoring\n", + " correction), singly robust via event model\n", + "- **AIPCW separable** (`Y_aipcw_sep_direct_astar1`, `Y_aipcw_sep_indirect_astar1`): doubly robust\n", + "\n", + "All variants are drop-in replacements for `Y_aipcw_total`, depending on the quantity of interest.\n", + "Learners below use the AIPCW variants." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cut_separable", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pseudo-outcome means (should: direct + indirect ≈ total):\n", + " Y_aipcw_total = 494.8028\n", + " Y_aipcw_sep_direct_astar1 = 552.7793\n", + " Y_aipcw_sep_indirect_astar1 = 436.5527\n", + " direct + indirect = 989.3319\n", + " Y_bj_sep_direct_astar1 = 551.8686 (drop-in BJ)\n", + " Y_bj_sep_indirect_astar1 = 437.5177 (drop-in BJ)\n" + ] + } + ], + "source": [ + "# AIPCW separable direct effect (A=1 pathway via relapse mechanism)\n", + "Y_aipcw_sep_direct_astar1 = aipcw_cut_rmtlj_sep_direct_astar1(\n", + " T, time_competing, event_competing, tau,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " time_grid=tg,\n", + ")\n", + "\n", + "# AIPCW separable indirect effect (A=1 pathway via NRM mechanism)\n", + "Y_aipcw_sep_indirect_astar1 = aipcw_cut_rmtlj_sep_indirect_astar1(\n", + " T, time_competing, event_competing, tau,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " time_grid=tg,\n", + ")\n", + "\n", + "# BJ separable variants (G=1, no censoring correction, singly robust via event model)\n", + "n_times = len(tg)\n", + "G_ones_a0 = np.ones((n, n_times))\n", + "G_ones_a1 = np.ones((n, n_times))\n", + "\n", + "Y_bj_sep_direct_astar1 = aipcw_cut_rmtlj_sep_direct_astar1(\n", + " T, time_competing, event_competing, tau,\n", + " G_ones_a0, G_ones_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " time_grid=tg,\n", + ")\n", + "\n", + "Y_bj_sep_indirect_astar1 = aipcw_cut_rmtlj_sep_indirect_astar1(\n", + " T, time_competing, event_competing, tau,\n", + " G_ones_a0, G_ones_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " time_grid=tg,\n", + ")\n", + "\n", + "print('Pseudo-outcome means (should: direct + indirect ≈ total):')\n", + "print(f' Y_aipcw_total = {Y_aipcw_total.mean():.4f}')\n", + "print(f' Y_aipcw_sep_direct_astar1 = {Y_aipcw_sep_direct_astar1.mean():.4f}')\n", + "print(f' Y_aipcw_sep_indirect_astar1 = {Y_aipcw_sep_indirect_astar1.mean():.4f}')\n", + "print(f' direct + indirect = {Y_aipcw_sep_direct_astar1.mean() + Y_aipcw_sep_indirect_astar1.mean():.4f}')\n", + "print(f' Y_bj_sep_direct_astar1 = {Y_bj_sep_direct_astar1.mean():.4f} (drop-in BJ)')\n", + "print(f' Y_bj_sep_indirect_astar1 = {Y_bj_sep_indirect_astar1.mean():.4f} (drop-in BJ)')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "learners_separable", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Separable effect ATEs:\n", + " Direct: SeparableDirectAstar1SLearner : ATE = 138.6149\n", + " Indirect: SeparableIndirectAstar1SLearner: ATE = 13.7198\n", + " Direct: SeparableDirectAstar1TLearner : ATE = 139.0223\n", + " Indirect: SeparableIndirectAstar1TLearner: ATE = 14.7058\n", + " Direct: AIPCW + TLearner : ATE = 198.6847\n", + " Direct: AIPCW + SLearner : ATE = 194.5839\n", + " Direct: AIPCW + CausalForest : ATE = 169.0818\n", + " Direct: IFLearner : ATE = 199.5983\n", + " Direct: AIPCW + XLearner : ATE = 208.0650\n", + " Direct: AIPCW + AIPTWLearner : ATE = 173.9401\n", + " Direct: AIPCW + RLearner : ATE = 218.0484\n", + " Direct: AIPCW + IPTWLearner : ATE = 234.5559\n", + " Direct: AIPCW + MCLearner : ATE = 95.6978\n", + " Direct: AIPCW + MCEALearner : ATE = 178.5350\n", + " Direct: AIPCW + ULearner : ATE = 162.5887\n", + " Direct: AIPCW + RALearner : ATE = 214.4284\n", + " Indirect: AIPCW + TLearner : ATE = 46.7501\n", + " Indirect: AIPCW + SLearner : ATE = 15.1512\n", + " Indirect: AIPCW + CausalForest : ATE = 45.9996\n", + " Indirect: IFLearner : ATE = 69.4826\n", + " Indirect: AIPCW + XLearner : ATE = 52.3154\n", + " Indirect: AIPCW + AIPTWLearner : ATE = 47.6934\n", + " Indirect: AIPCW + RLearner : ATE = 42.8761\n", + " Indirect: AIPCW + IPTWLearner : ATE = 19.1483\n", + " Indirect: AIPCW + MCLearner : ATE = -109.2542\n", + " Indirect: AIPCW + MCEALearner : ATE = 41.7126\n", + " Indirect: AIPCW + ULearner : ATE = 48.9489\n", + " Indirect: AIPCW + RALearner : ATE = 35.0474\n" + ] + } + ], + "source": [ + "results_sep = {}\n", + "\n", + "# Direct competing-risk learners with separable-effect support\n", + "cr_s_sep_direct = SeparableDirectAstar1SLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "cr_s_sep_direct.fit(competing_obj, T, X=X)\n", + "direct_cr_s = clip_hte(cr_s_sep_direct.effect(X))\n", + "results_sep['Direct: SeparableDirectAstar1SLearner'] = direct_cr_s\n", + "\n", + "cr_s_sep_indirect = SeparableIndirectAstar1SLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "cr_s_sep_indirect.fit(competing_obj, T, X=X)\n", + "indirect_cr_s = clip_hte(cr_s_sep_indirect.effect(X))\n", + "results_sep['Indirect: SeparableIndirectAstar1SLearner'] = indirect_cr_s\n", + "\n", + "cr_t_sep_direct = SeparableDirectAstar1TLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "cr_t_sep_direct.fit(competing_obj, T, X=X)\n", + "direct_cr_t = clip_hte(cr_t_sep_direct.effect(X))\n", + "results_sep['Direct: SeparableDirectAstar1TLearner'] = direct_cr_t\n", + "\n", + "cr_t_sep_indirect = SeparableIndirectAstar1TLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "cr_t_sep_indirect.fit(competing_obj, T, X=X)\n", + "indirect_cr_t = clip_hte(cr_t_sep_indirect.effect(X))\n", + "results_sep['Indirect: SeparableIndirectAstar1TLearner'] = indirect_cr_t\n", + "\n", + "# UIF separable pseudo-outcomes\n", + "ps = nuis.ps\n", + "bw = nuis.iptw\n", + "tilt = nuis.naive\n", + "Y_uif_diff_sep_direct_astar1 = uif_diff_rmtlj_sep_direct_astar1(\n", + " T, ps, time_competing, event_competing, tau,\n", + " bw, tilt,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg,\n", + ")\n", + "Y_uif_diff_sep_indirect_astar1 = uif_diff_rmtlj_sep_indirect_astar1(\n", + " T, ps, time_competing, event_competing, tau,\n", + " bw, tilt,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " nuis.Sj_a0, nuis.Sj_a1,\n", + " nuis.Sjbar_a0, nuis.Sjbar_a1,\n", + " cause=1, time_grid=tg,\n", + ")\n", + "\n", + "# --- Direct effect learners ---\n", + "tl_direct = TLearner(cv=2)\n", + "tl_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + TLearner'] = clip_hte(tl_direct.effect(X))\n", + "\n", + "sl_direct = SLearner(cv=2)\n", + "sl_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + SLearner'] = clip_hte(sl_direct.effect(X))\n", + "\n", + "cf_direct = CausalForest(num_trees=200, seed=0)\n", + "cf_direct.fit(X, Y_aipcw_sep_direct_astar1, T)\n", + "results_sep['Direct: AIPCW + CausalForest'] = clip_hte(cf_direct.effect(X))\n", + "\n", + "if_direct = IFLearner(cv=2)\n", + "if_direct.fit(Y_uif_diff_sep_direct_astar1, X=X)\n", + "results_sep['Direct: IFLearner'] = clip_hte(if_direct.effect(X))\n", + "\n", + "x_direct = XLearner(cv=2)\n", + "x_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + XLearner'] = clip_hte(x_direct.effect(X))\n", + "\n", + "aiptw_direct = AIPTWLearner(cv=2)\n", + "aiptw_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + AIPTWLearner'] = clip_hte(aiptw_direct.effect(X))\n", + "\n", + "r_direct = RLearner(cv=2)\n", + "r_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + RLearner'] = clip_hte(r_direct.effect(X))\n", + "\n", + "iptw_direct = IPTWLearner(cv=2)\n", + "iptw_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + IPTWLearner'] = clip_hte(iptw_direct.effect(X))\n", + "\n", + "mc_direct = MCLearner(cv=2)\n", + "mc_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + MCLearner'] = clip_hte(mc_direct.effect(X))\n", + "\n", + "mcea_direct = MCEALearner(cv=2)\n", + "mcea_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + MCEALearner'] = clip_hte(mcea_direct.effect(X))\n", + "\n", + "u_direct = ULearner(cv=2)\n", + "u_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + ULearner'] = clip_hte(u_direct.effect(X))\n", + "\n", + "ra_direct = RALearner(cv=2)\n", + "ra_direct.fit(Y_aipcw_sep_direct_astar1, T, X=X)\n", + "results_sep['Direct: AIPCW + RALearner'] = clip_hte(ra_direct.effect(X))\n", + "\n", + "# --- Indirect effect learners ---\n", + "tl_indirect = TLearner(cv=2)\n", + "tl_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + TLearner'] = clip_hte(tl_indirect.effect(X))\n", + "\n", + "sl_indirect = SLearner(cv=2)\n", + "sl_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + SLearner'] = clip_hte(sl_indirect.effect(X))\n", + "\n", + "cf_indirect = CausalForest(num_trees=200, seed=0)\n", + "cf_indirect.fit(X, Y_aipcw_sep_indirect_astar1, T)\n", + "results_sep['Indirect: AIPCW + CausalForest'] = clip_hte(cf_indirect.effect(X))\n", + "\n", + "if_indirect = IFLearner(cv=2)\n", + "if_indirect.fit(Y_uif_diff_sep_indirect_astar1, X=X)\n", + "results_sep['Indirect: IFLearner'] = clip_hte(if_indirect.effect(X))\n", + "\n", + "x_indirect = XLearner(cv=2)\n", + "x_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + XLearner'] = clip_hte(x_indirect.effect(X))\n", + "\n", + "aiptw_indirect = AIPTWLearner(cv=2)\n", + "aiptw_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + AIPTWLearner'] = clip_hte(aiptw_indirect.effect(X))\n", + "\n", + "r_indirect = RLearner(cv=2)\n", + "r_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + RLearner'] = clip_hte(r_indirect.effect(X))\n", + "\n", + "iptw_indirect = IPTWLearner(cv=2)\n", + "iptw_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + IPTWLearner'] = clip_hte(iptw_indirect.effect(X))\n", + "\n", + "mc_indirect = MCLearner(cv=2)\n", + "mc_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + MCLearner'] = clip_hte(mc_indirect.effect(X))\n", + "\n", + "mcea_indirect = MCEALearner(cv=2)\n", + "mcea_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + MCEALearner'] = clip_hte(mcea_indirect.effect(X))\n", + "\n", + "u_indirect = ULearner(cv=2)\n", + "u_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + ULearner'] = clip_hte(u_indirect.effect(X))\n", + "\n", + "ra_indirect = RALearner(cv=2)\n", + "ra_indirect.fit(Y_aipcw_sep_indirect_astar1, T, X=X)\n", + "results_sep['Indirect: AIPCW + RALearner'] = clip_hte(ra_indirect.effect(X))\n", + "\n", + "print('Separable effect ATEs:')\n", + "for name, cate in results_sep.items():\n", + " print(f' {name:40s}: ATE = {cate.mean():.4f}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "section5_header", + "metadata": {}, + "source": [ + "## Section 5: Mortality — Single-Cause RMST\n", + "\n", + "Maps to `mortality_hte.R`. The outcome is non-relapse mortality (NRM), modeled as a single-cause\n", + "survival problem (no competing risks)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "nuisance_mortality", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting nuisance models for mortality (single cause)...\n", + "Done. Time grid: 1661 points\n", + "Y_ipcw_mortality: mean=1028.86\n", + "Y_bj_mortality: mean=1141.15\n", + "Y_aipcw_mortality: mean=1119.80\n" + ] + } + ], + "source": [ + "print('Fitting nuisance models for mortality (single cause)...')\n", + "nuis_mort = fit_nuisance_survival(\n", + " time_mortality, event_mortality, T, X,\n", + " cv=2,\n", + ")\n", + "tg_mort = nuis_mort.time_grid\n", + "print(f'Done. Time grid: {len(tg_mort)} points')\n", + "\n", + "# IPCW CUT (drop-in, singly robust)\n", + "Y_ipcw_mortality = ipcw_cut_rmst(\n", + " T, time_mortality, event_mortality, tau,\n", + " nuis_mort.G_a0, nuis_mort.G_a1,\n", + " time_grid=tg_mort,\n", + ")\n", + "\n", + "# BJ CUT (drop-in, singly robust)\n", + "Y_bj_mortality = bj_cut_rmst(\n", + " T, time_mortality, event_mortality, tau,\n", + " nuis_mort.S_a0, nuis_mort.S_a1,\n", + " time_grid=tg_mort,\n", + ")\n", + "\n", + "# AIPCW CUT (doubly robust) — primary\n", + "Y_aipcw_mortality = aipcw_cut_rmst(\n", + " T, time_mortality, event_mortality, tau,\n", + " nuis_mort.G_a0, nuis_mort.G_a1,\n", + " nuis_mort.S_a0, nuis_mort.S_a1,\n", + " time_grid=tg_mort,\n", + ")\n", + "\n", + "print(f'Y_ipcw_mortality: mean={Y_ipcw_mortality.mean():.2f}')\n", + "print(f'Y_bj_mortality: mean={Y_bj_mortality.mean():.2f}')\n", + "print(f'Y_aipcw_mortality: mean={Y_aipcw_mortality.mean():.2f}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "learners_mortality", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mortality ATEs (positive = RIC increases RMST for NRM = harmful):\n", + " SurvivalSLearner : ATE = 60.7100\n", + " SurvivalTLearner : ATE = 64.3636\n", + " CausalSurvivalForest : ATE = 82.4836\n", + " AIPCW + TLearner : ATE = 72.2084\n", + " AIPCW + SLearner : ATE = 48.8001\n", + " AIPCW + CausalForest : ATE = 86.5761\n", + " IFLearner : ATE = 71.1691\n", + " AIPCW + XLearner : ATE = 82.8100\n", + " AIPCW + AIPTWLearner : ATE = 69.1782\n", + " AIPCW + RLearner : ATE = 58.3835\n", + " AIPCW + IPTWLearner : ATE = 94.5716\n", + " AIPCW + MCLearner : ATE = -225.6462\n", + " AIPCW + MCEALearner : ATE = 50.2748\n", + " AIPCW + ULearner : ATE = 35.4817\n", + " AIPCW + RALearner : ATE = 66.9431\n" + ] + } + ], + "source": [ + "results_mort = {}\n", + "\n", + "# Direct survival-object input for single-cause mortality\n", + "surv_obj = np.array(\n", + " [(bool(e), t) for e, t in zip(event_mortality, time_mortality)],\n", + " dtype=[('event', bool), ('time', float)],\n", + ")\n", + "\n", + "sv_slearner = SurvivalSLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "sv_slearner.fit(surv_obj, T, X=X)\n", + "results_mort['SurvivalSLearner'] = clip_hte(sv_slearner.effect(X))\n", + "\n", + "sv_tlearner = SurvivalTLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "sv_tlearner.fit(surv_obj, T, X=X)\n", + "results_mort['SurvivalTLearner'] = clip_hte(sv_tlearner.effect(X))\n", + "\n", + "csf_mort = CausalSurvivalForest(tau=tau, n_estimators=200, nuisance_cv=2, random_state=0)\n", + "csf_mort.fit(X, T, time_mortality, event_mortality.astype(bool))\n", + "results_mort['CausalSurvivalForest'] = clip_hte(csf_mort.effect(X))\n", + "\n", + "tl_mort = TLearner(cv=2)\n", + "tl_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + TLearner'] = clip_hte(tl_mort.effect(X))\n", + "\n", + "sl_mort = SLearner(cv=2)\n", + "sl_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + SLearner'] = clip_hte(sl_mort.effect(X))\n", + "\n", + "cf_mort = CausalForest(num_trees=200, seed=0)\n", + "cf_mort.fit(X, Y_aipcw_mortality, T)\n", + "results_mort['AIPCW + CausalForest'] = clip_hte(cf_mort.effect(X))\n", + "\n", + "bw_mort = nuis_mort.iptw\n", + "tilt_mort = nuis_mort.naive\n", + "Y_uif_diff_mortality = uif_diff_rmst(\n", + " T, time_mortality, event_mortality, tau,\n", + " bw_mort, tilt_mort,\n", + " nuis_mort.G_a0, nuis_mort.G_a1,\n", + " nuis_mort.S_a0, nuis_mort.S_a1,\n", + " time_grid=tg_mort,\n", + ")\n", + "if_mort = IFLearner(cv=2)\n", + "if_mort.fit(Y_uif_diff_mortality, X=X)\n", + "results_mort['IFLearner'] = clip_hte(if_mort.effect(X))\n", + "\n", + "xl_mort = XLearner(cv=2)\n", + "xl_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + XLearner'] = clip_hte(xl_mort.effect(X))\n", + "\n", + "aiptw_mort = AIPTWLearner(cv=2)\n", + "aiptw_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + AIPTWLearner'] = clip_hte(aiptw_mort.effect(X))\n", + "\n", + "r_mort = RLearner(cv=2)\n", + "r_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + RLearner'] = clip_hte(r_mort.effect(X))\n", + "\n", + "iptw_mort = IPTWLearner(cv=2)\n", + "iptw_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + IPTWLearner'] = clip_hte(iptw_mort.effect(X))\n", + "\n", + "mc_mort = MCLearner(cv=2)\n", + "mc_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + MCLearner'] = clip_hte(mc_mort.effect(X))\n", + "\n", + "mcea_mort = MCEALearner(cv=2)\n", + "mcea_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + MCEALearner'] = clip_hte(mcea_mort.effect(X))\n", + "\n", + "u_mort = ULearner(cv=2)\n", + "u_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + ULearner'] = clip_hte(u_mort.effect(X))\n", + "\n", + "ra_mort = RALearner(cv=2)\n", + "ra_mort.fit(Y_aipcw_mortality, T, X=X)\n", + "results_mort['AIPCW + RALearner'] = clip_hte(ra_mort.effect(X))\n", + "\n", + "print('Mortality ATEs (positive = RIC increases RMST for NRM = harmful):')\n", + "for name, cate in results_mort.items():\n", + " print(f' {name:35s}: ATE = {cate.mean():.4f}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "section6_header", + "metadata": {}, + "source": [ + "## Section 6: Composite Endpoint (Relapse or Mortality)\n", + "\n", + "Maps to `composite_hte.R`. The outcome is any event (relapse or NRM), treated as a single-cause\n", + "survival problem." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "nuisance_composite", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting nuisance models for composite endpoint...\n", + "Done. Time grid: 1493 points\n", + "Y_ipcw_composite: mean=617.79\n", + "Y_bj_composite: mean=690.95\n", + "Y_aipcw_composite: mean=681.12\n" + ] + } + ], + "source": [ + "print('Fitting nuisance models for composite endpoint...')\n", + "nuis_comp = fit_nuisance_survival(\n", + " time_composite, event_composite, T, X,\n", + " cv=2,\n", + ")\n", + "tg_comp = nuis_comp.time_grid\n", + "print(f'Done. Time grid: {len(tg_comp)} points')\n", + "\n", + "# IPCW CUT (drop-in)\n", + "Y_ipcw_composite = ipcw_cut_rmst(\n", + " T, time_composite, event_composite, tau,\n", + " nuis_comp.G_a0, nuis_comp.G_a1,\n", + " time_grid=tg_comp,\n", + ")\n", + "\n", + "# BJ CUT (drop-in)\n", + "Y_bj_composite = bj_cut_rmst(\n", + " T, time_composite, event_composite, tau,\n", + " nuis_comp.S_a0, nuis_comp.S_a1,\n", + " time_grid=tg_comp,\n", + ")\n", + "\n", + "# AIPCW CUT (primary)\n", + "Y_aipcw_composite = aipcw_cut_rmst(\n", + " T, time_composite, event_composite, tau,\n", + " nuis_comp.G_a0, nuis_comp.G_a1,\n", + " nuis_comp.S_a0, nuis_comp.S_a1,\n", + " time_grid=tg_comp,\n", + ")\n", + "\n", + "print(f'Y_ipcw_composite: mean={Y_ipcw_composite.mean():.2f}')\n", + "print(f'Y_bj_composite: mean={Y_bj_composite.mean():.2f}')\n", + "print(f'Y_aipcw_composite: mean={Y_aipcw_composite.mean():.2f}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "learners_composite", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Composite endpoint ATEs (positive = RIC increases RMST for event = harmful):\n", + " SurvivalSLearner : ATE = -80.5582\n", + " SurvivalTLearner : ATE = -82.5060\n", + " CausalSurvivalForest : ATE = -71.1720\n", + " AIPCW + TLearner : ATE = -80.0155\n", + " AIPCW + SLearner : ATE = -67.9199\n", + " AIPCW + CausalForest : ATE = -72.4060\n", + " IFLearner : ATE = -83.6547\n", + " AIPCW + XLearner : ATE = -67.4980\n", + " AIPCW + AIPTWLearner : ATE = -77.8742\n", + " AIPCW + RLearner : ATE = -72.9002\n", + " AIPCW + IPTWLearner : ATE = -56.8673\n", + " AIPCW + MCLearner : ATE = -252.6209\n", + " AIPCW + MCEALearner : ATE = -61.3109\n", + " AIPCW + ULearner : ATE = -71.4401\n", + " AIPCW + RALearner : ATE = -68.1386\n" + ] + } + ], + "source": [ + "results_comp = {}\n", + "\n", + "# Direct survival-object input for the composite endpoint\n", + "composite_obj = np.array(\n", + " [(bool(e), t) for e, t in zip(event_composite, time_composite)],\n", + " dtype=[('event', bool), ('time', float)],\n", + ")\n", + "\n", + "sv_sl_comp = SurvivalSLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "sv_sl_comp.fit(composite_obj, T, X=X)\n", + "results_comp['SurvivalSLearner'] = clip_hte(sv_sl_comp.effect(X))\n", + "\n", + "sv_tl_comp = SurvivalTLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "sv_tl_comp.fit(composite_obj, T, X=X)\n", + "results_comp['SurvivalTLearner'] = clip_hte(sv_tl_comp.effect(X))\n", + "\n", + "csf_comp = CausalSurvivalForest(tau=tau, n_estimators=200, nuisance_cv=2, random_state=0)\n", + "csf_comp.fit(X, T, time_composite, event_composite.astype(bool))\n", + "results_comp['CausalSurvivalForest'] = clip_hte(csf_comp.effect(X))\n", + "\n", + "tl_comp = TLearner(cv=2)\n", + "tl_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + TLearner'] = clip_hte(tl_comp.effect(X))\n", + "\n", + "sl_comp = SLearner(cv=2)\n", + "sl_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + SLearner'] = clip_hte(sl_comp.effect(X))\n", + "\n", + "cf_comp = CausalForest(num_trees=200, seed=0)\n", + "cf_comp.fit(X, Y_aipcw_composite, T)\n", + "results_comp['AIPCW + CausalForest'] = clip_hte(cf_comp.effect(X))\n", + "\n", + "bw_comp = nuis_comp.iptw\n", + "tilt_comp = nuis_comp.naive\n", + "Y_uif_diff_composite = uif_diff_rmst(\n", + " T, time_composite, event_composite, tau,\n", + " bw_comp, tilt_comp,\n", + " nuis_comp.G_a0, nuis_comp.G_a1,\n", + " nuis_comp.S_a0, nuis_comp.S_a1,\n", + " time_grid=tg_comp,\n", + ")\n", + "if_comp = IFLearner(cv=2)\n", + "if_comp.fit(Y_uif_diff_composite, X=X)\n", + "results_comp['IFLearner'] = clip_hte(if_comp.effect(X))\n", + "\n", + "xl_comp = XLearner(cv=2)\n", + "xl_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + XLearner'] = clip_hte(xl_comp.effect(X))\n", + "\n", + "aiptw_comp = AIPTWLearner(cv=2)\n", + "aiptw_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + AIPTWLearner'] = clip_hte(aiptw_comp.effect(X))\n", + "\n", + "r_comp = RLearner(cv=2)\n", + "r_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + RLearner'] = clip_hte(r_comp.effect(X))\n", + "\n", + "iptw_comp = IPTWLearner(cv=2)\n", + "iptw_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + IPTWLearner'] = clip_hte(iptw_comp.effect(X))\n", + "\n", + "mc_comp = MCLearner(cv=2)\n", + "mc_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + MCLearner'] = clip_hte(mc_comp.effect(X))\n", + "\n", + "mcea_comp = MCEALearner(cv=2)\n", + "mcea_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + MCEALearner'] = clip_hte(mcea_comp.effect(X))\n", + "\n", + "u_comp = ULearner(cv=2)\n", + "u_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + ULearner'] = clip_hte(u_comp.effect(X))\n", + "\n", + "ra_comp = RALearner(cv=2)\n", + "ra_comp.fit(Y_aipcw_composite, T, X=X)\n", + "results_comp['AIPCW + RALearner'] = clip_hte(ra_comp.effect(X))\n", + "\n", + "print('Composite endpoint ATEs (positive = RIC increases RMST for event = harmful):')\n", + "for name, cate in results_comp.items():\n", + " print(f' {name:35s}: ATE = {cate.mean():.4f}')\n" + ] + }, + { + "cell_type": "markdown", + "id": "section7_header", + "metadata": {}, + "source": [ + "## Section 7: CATE Estimates & Distribution Comparison\n", + "\n", + "This section compares patient-level CATE estimates across all learners for five analyses:\n", + "\n", + "1. relapse total effect\n", + "2. relapse separable direct effect\n", + "3. relapse separable indirect effect\n", + "4. mortality single-cause effect\n", + "5. composite endpoint single-cause effect\n", + "\n", + "For each analysis we report:\n", + "- a CATE summary table across learners\n", + "- a boxplot of the learner-wise CATE distributions\n", + "\n", + "Since this is a real-data case study, these plots compare learner outputs directly rather than\n", + "against a known ground-truth CATE.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "summary_ate", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Relapse total CATE\n", + "==================\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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 LearnerMeanStdMinQ25MedianQ75Max
0AIPCW + AIPTWLearner165.42316.46-1265.40-30.44168.26370.081343.54
1AIPCW + CausalForest170.41110.13-201.5196.32170.53243.29580.60
2AIPCW + IPTWLearner178.76473.70-1268.63-124.94156.67448.672922.00
3AIPCW + MCEALearner149.00290.55-920.53-47.20148.27348.711104.43
4AIPCW + MCLearner16.76464.22-1626.86-294.5635.04343.541465.63
5AIPCW + RALearner146.93199.32-528.1512.44152.30280.92797.03
6AIPCW + RLearner153.24336.77-1148.44-62.92162.68380.791359.26
7AIPCW + SLearner153.69145.77-359.2647.87146.67252.29749.80
8AIPCW + TLearner145.46176.42-526.9529.30148.58262.59786.18
9AIPCW + ULearner130.27562.42-2922.00-149.52136.04425.292922.00
10AIPCW + XLearner159.84153.49-426.2557.46153.33259.25672.13
11CompetingRisksSLearner154.01171.05-498.1842.13155.60265.49825.58
12CompetingRisksTLearner151.87157.69-488.0551.93163.64258.61712.63
13IFLearner (total)277.52935.45-2922.00-273.62235.78797.132922.00
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 LearnerMeanStdMinQ25MedianQ75Max
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1AIPCW + CausalForest169.08266.31-814.70-4.11155.86340.021469.61
2AIPCW + IPTWLearner234.561005.51-2922.00-400.21160.17794.982922.00
3AIPCW + MCEALearner178.53867.45-2922.00-346.85141.84658.282922.00
4AIPCW + MCLearner95.70885.58-2907.24-479.8771.87649.972922.00
5AIPCW + RALearner214.43568.69-2093.26-148.31215.62576.162352.88
6AIPCW + RLearner218.05934.50-2922.00-380.82202.29786.782922.00
7AIPCW + SLearner194.58395.26-1186.75-72.99175.31441.601897.23
8AIPCW + TLearner198.68433.97-1392.31-79.67199.13473.641901.52
9AIPCW + ULearner162.591272.89-2922.00-602.49146.53885.792922.00
10AIPCW + XLearner208.06435.77-1441.38-71.16201.73492.692373.99
11IFLearner199.60948.35-2922.00-365.64188.30741.472922.00
12SeparableDirectAstar1SLearner138.61164.23-313.2922.49132.49249.19698.63
13SeparableDirectAstar1TLearner139.02151.45-451.5837.41140.54237.19780.73
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 LearnerMeanStdMinQ25MedianQ75Max
0AIPCW + AIPTWLearner47.69190.83-786.27-67.1325.66142.371577.51
1AIPCW + CausalForest46.0068.98-196.77-0.6135.2380.36433.01
2AIPCW + IPTWLearner19.15314.72-1442.24-178.6329.28216.781547.73
3AIPCW + MCEALearner41.71236.13-1261.08-88.4114.38142.931993.95
4AIPCW + MCLearner-109.25388.13-2057.52-350.90-76.73155.531340.37
5AIPCW + RALearner35.05151.93-727.78-54.4528.11110.411217.53
6AIPCW + RLearner42.88263.18-1358.63-109.0310.55160.792130.48
7AIPCW + SLearner15.1569.59-213.41-27.198.3054.19356.67
8AIPCW + TLearner46.75138.90-605.83-38.5226.07108.20839.04
9AIPCW + ULearner48.95498.35-2922.00-156.9221.69223.652922.00
10AIPCW + XLearner52.32106.67-361.41-15.0833.59102.58590.88
11IFLearner69.48321.84-2922.00-80.3650.07206.641801.13
12SeparableIndirectAstar1SLearner13.7233.56-161.86-5.2811.4932.32164.08
13SeparableIndirectAstar1TLearner14.7135.87-135.83-5.7512.5533.14188.78
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 LearnerMeanStdMinQ25MedianQ75Max
0AIPCW + AIPTWLearner69.18305.99-1380.53-120.2776.43255.041216.42
1AIPCW + CausalForest86.5892.31-360.3927.1090.01149.84424.14
2AIPCW + IPTWLearner94.57664.69-2021.92-381.75102.94547.512922.00
3AIPCW + MCEALearner50.27252.52-994.75-104.7745.56208.571206.72
4AIPCW + MCLearner-225.65834.65-2770.04-783.52-161.73375.731992.93
5AIPCW + RALearner66.94157.44-536.26-38.8962.65170.17696.36
6AIPCW + RLearner58.38305.31-1276.13-132.0758.05252.001530.22
7AIPCW + SLearner48.8099.73-356.68-12.0743.26110.14475.10
8AIPCW + TLearner72.21146.03-432.85-28.0766.94162.35738.25
9AIPCW + ULearner35.48464.16-2572.30-213.2548.06291.712922.00
10AIPCW + XLearner82.81125.26-358.233.9083.48163.29485.21
11CausalSurvivalForest82.48102.27-251.8411.7279.64147.27505.15
12IFLearner71.17314.48-2922.00-88.9282.32262.591177.46
13SurvivalSLearner60.71109.64-310.12-12.9959.13132.78502.79
14SurvivalTLearner64.36124.48-373.84-19.7364.33144.16520.86
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Composite endpoint single-cause CATE\n", + "====================================\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 LearnerMeanStdMinQ25MedianQ75Max
0AIPCW + AIPTWLearner-77.87311.17-1211.51-280.26-73.11120.491478.04
1AIPCW + CausalForest-72.4199.53-409.96-137.08-71.36-5.73241.31
2AIPCW + IPTWLearner-56.87474.28-1351.99-384.20-78.91264.662329.23
3AIPCW + MCEALearner-61.31279.23-919.93-252.79-65.75131.64993.24
4AIPCW + MCLearner-252.62541.85-2348.48-623.37-220.10137.451293.86
5AIPCW + RALearner-68.14181.50-623.66-189.09-71.9451.61470.75
6AIPCW + RLearner-72.90309.56-1330.92-279.73-66.62139.56963.54
7AIPCW + SLearner-67.92131.62-642.78-152.83-52.0925.79257.44
8AIPCW + TLearner-80.02154.05-534.80-186.92-86.3320.86524.91
9AIPCW + ULearner-71.44608.73-2922.00-361.83-72.67210.452922.00
10AIPCW + XLearner-67.50161.22-509.79-178.33-75.0732.52432.78
11CausalSurvivalForest-71.1764.82-345.20-115.10-72.98-29.25247.14
12IFLearner-83.65315.51-1756.95-281.84-79.17112.31936.78
13SurvivalSLearner-80.56132.24-620.66-166.33-83.335.83356.87
14SurvivalTLearner-82.51135.94-504.78-180.73-84.239.60388.96
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "analysis_results = {\n", + " 'Relapse total CATE': results_total,\n", + " 'Relapse separable direct CATE': {k.split(': ', 1)[1]: v for k, v in results_sep.items() if k.startswith('Direct: ')},\n", + " 'Relapse separable indirect CATE': {k.split(': ', 1)[1]: v for k, v in results_sep.items() if k.startswith('Indirect: ')},\n", + " 'Mortality single-cause CATE': results_mort,\n", + " 'Composite endpoint single-cause CATE': results_comp,\n", + "}\n", + "\n", + "def cate_summary_table(results_dict):\n", + " rows = []\n", + " for learner, cate in results_dict.items():\n", + " cate = np.asarray(cate).ravel()\n", + " rows.append({\n", + " 'Learner': learner,\n", + " 'Mean': cate.mean(),\n", + " 'Std': cate.std(),\n", + " 'Min': cate.min(),\n", + " 'Q25': np.quantile(cate, 0.25),\n", + " 'Median': np.median(cate),\n", + " 'Q75': np.quantile(cate, 0.75),\n", + " 'Max': cate.max(),\n", + " })\n", + " return pd.DataFrame(rows).sort_values('Learner').reset_index(drop=True)\n", + "\n", + "cate_tables = {}\n", + "for analysis_name, results_dict in analysis_results.items():\n", + " print(f\"\\n{analysis_name}\")\n", + " print('=' * len(analysis_name))\n", + " table = cate_summary_table(results_dict)\n", + " cate_tables[analysis_name] = table\n", + " display(table.style.format({\n", + " 'Mean': '{:.2f}',\n", + " 'Std': '{:.2f}',\n", + " 'Min': '{:.2f}',\n", + " 'Q25': '{:.2f}',\n", + " 'Median': '{:.2f}',\n", + " 'Q75': '{:.2f}',\n", + " 'Max': '{:.2f}',\n", + " }))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6120468c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_cate_boxplot(title, results_dict):\n", + " learner_names = list(results_dict.keys())\n", + " cate_arrays = [np.asarray(results_dict[name]).ravel() for name in learner_names]\n", + " fig_width = max(12, 0.7 * len(learner_names))\n", + " fig, ax = plt.subplots(figsize=(fig_width, 5.5))\n", + " ax.boxplot(cate_arrays, showmeans=True, widths=0.8)\n", + " ax.axhline(0, color='red', linestyle='--', alpha=0.6, label='No effect')\n", + " ax.set_xticks(np.arange(1, len(learner_names) + 1))\n", + " ax.set_xticklabels(learner_names, rotation=45, ha='right')\n", + " ax.set_title(title)\n", + " ax.set_ylabel('Estimated CATE (days)')\n", + " ax.legend(loc='upper right')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "for analysis_name, results_dict in analysis_results.items():\n", + " plot_cate_boxplot(f\"{analysis_name} — Boxplot\", results_dict)\n" + ] + }, + { + "cell_type": "markdown", + "id": "notes", + "metadata": {}, + "source": [ + "## Notes\n", + "\n", + "### What is not reproduced here\n", + "- `relapse_cde_hte.R` (controlled direct effect / CDE) — this is a methodological variant\n", + " requiring a different identification strategy and is not covered in the current Python implementation.\n", + "\n", + "### Simplifications vs. the original R analyses\n", + "- **Nuisance/final models**: The original R analyses used `survSuperLearner` and `SuperLearner` ensembles. Here the notebook uses the package defaults: `RandomSurvivalForest` for censored nuisances and direct censored-outcome learners, `RandomForestClassifier` for binary nuisances, and `RandomForestRegressor` for continuous nuisances and pseudo-outcome final models.\n", + "- **Cross-fitting**: The original analyses used 2-fold cross-fitting (sample splitting). This notebook also uses 2-fold cross-fitting in the nuisance helpers and the learners imported from `econml.metalearners`.\n", + "\n", + "### References\n", + "- Bejanyan N et al. (2021). *Myeloablative versus Reduced-Intensity Conditioning prior to\n", + " Hematopoietic Cell Transplantation for Myeloid Malignancies*. Blood Advances.\n", + "- CIBMTR dataset: https://cibmtr.org/Manuscript/a020h00001F1sihAAB/P-5370" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Solutions/lk1602 data dictionary for public sharing.rtf b/notebooks/Solutions/lk1602 data dictionary for public sharing.rtf new file mode 100644 index 000000000..c1c392cde --- /dev/null +++ b/notebooks/Solutions/lk1602 data dictionary for public sharing.rtf @@ -0,0 +1,2264 @@ +{\rtf1\ansi\ansicpg1252\cocoartf2761 +\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 Helvetica-Bold;\f1\fswiss\fcharset0 Helvetica;\f2\froman\fcharset0 TimesNewRomanPSMT; +} +{\colortbl;\red255\green255\blue255;\red191\green191\blue191;} +{\*\expandedcolortbl;;\csgray\c79525;} +{\info +{\title V9.4 SAS System Output} +{\author SAS Version 9.4}}\paperw11900\paperh16840\margl1440\margr1440\vieww12500\viewh13420\viewkind1\viewscale110 +\deftab720 +\pard\pardeftab720\ri-5\sb10\sa10\partightenfactor0 + +\f0\b\fs22 \cf0 Data dictionary for data set: lk1602public.sas7bdat\ +\pard\pardeftab720\ri-5\partightenfactor0 +\cf0 \ + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrt\brdrnil \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalb \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf0 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalb \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf0 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalb \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf0 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalb \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf0 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalb \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf0 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalb \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf0 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 #\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Variable\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qc\partightenfactor0 +\cf0 Description\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qc\partightenfactor0 +\cf0 Type\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 Value\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qc\partightenfactor0 +\cf0 Label\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 + +\f1\b0 \cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 dummycrid\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Dummy patient ID\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Continuous\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 dummyccn\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Dummy center ID\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Continuous\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 strataf\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Study main effect\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 MAC and Low/Intermediate risk\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 MAC and High/Very High risk\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 RIC/NMA and Low/Intermediate risk\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 RIC/NMA and High/Very High Risk\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 disease\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Disease\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 10\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 AML\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 50\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 MDS\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 5\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 agegpff\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Age at HCT, years\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 40-50\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 51-60\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 61+\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clheight553 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clheight553 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clheight553 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clheight553 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clheight553 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clheight553 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 6\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 sex\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Recipient Sex\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Male\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Female\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 7\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 kps\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Karnofsky score\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 0\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 <90\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 >=90\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 99\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 8\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 hctcigpc\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 No label\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 5\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 5+\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 99\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 9\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 amstatprgp\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Disease status prior to HCT, for AML\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Primary induction failure\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 CR1\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 CR2\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 >=CR3\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 5\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Relapse\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 6\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 No treatment\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 7\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 NA(MDS)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 10\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 distatus\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Disease status prior to HCT, MDS only\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Early\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Advanced\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Other\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 NA(AML)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 11\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 wbcdxgp\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 White blood count at diagnosis,(x 10^9) AML only\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 <1-10\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 11-100\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 101+\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 NA(MDS)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 99\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 12\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 donorgp_final\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Donor type\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Matched related Donor\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Mismatched relative (>=7/8) / Other relatives (missing HLA)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Matched unrelated donor (8/8)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Unrelated (<=7/8) and matching unknown\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 5\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 UCB donor\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 13\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 drsex\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Donor/recipient sex match\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 M-M\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 M-F\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 F-M\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 F-F\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 99\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 14\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 drcmvprf\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Donor/Recipient CMV serostatus\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Rec +\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Rec-/Donor -\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Other\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 15\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 condgrp\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Conditioning regimen\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/Cy\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/Cy/Flu\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/Cy/Flu/TT\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/Cy/TT\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 5\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/Cy/VP\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 6\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/VP\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 7\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/Mel\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 8\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/Flu\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 9\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TBI/other(s)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 10\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Bu/Cy/Mel\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 11\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Bu/Cy\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 12\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Bu/Mel\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 13\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Flu/Bu/TT\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 14\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Flu/Bu\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 15\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Flu/Mel/TT\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 16\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Flu/Mel\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 17\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 FCR\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 18\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Cy/Flu\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 19\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Cy alone\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 20\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 CBV\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 21\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 BEAM\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 22\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 BEAM like\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 23\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Mel alone\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 24\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Mel/other(s)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clheight11540 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clheight11540 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clheight11540 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clheight11540 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clheight11540 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clheight11540 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 25\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Treosulfan\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 26\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Carb/Etop\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 27\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Carb/other(s)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 28\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TLI\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 90\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Other(s)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 98\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 None\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 99\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 16\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 gvhdgpf\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 GVHD prophylaxis\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 TCD/CD34\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Tac + MMF/MTX +/- others\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clheight10960 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clheight10960 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clheight10960 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clheight10960 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clheight10960 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clheight10960 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 3\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 CSA + MMF/MTX +/- others\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 4\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Post-cy + others\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 5\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Others/Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 99\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 17\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 atggrp\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 (ATG/Alemtuzumab) for conditioning regimen or GVHD prophylaxis\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Yes(ATG/Alemtuzumab)\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 2\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 No\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 99\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Missing\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 18\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 graftype\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Graft source\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Bone marrow\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 22\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Peripheral blood\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 23\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Cord blood\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 19\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 yeartx\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Year of transplant\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Continuous\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 20\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 rel\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Relapse\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 0\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Censor\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Event\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 21\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 trm\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Treatment related mortality\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 0\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Censor\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Event\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clheight1780 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clheight1780 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clheight1780 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clheight1780 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clheight1780 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clheight1780 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 22\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 dfs\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Disease free survival\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Categorical\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 0\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Censor\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 1\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Event\cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt \clcbpat1 \clwWidth2862\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx8640 +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 23\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f0\b \cf0 intxrel\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 + +\f1\b0 \cf0 Time from HCT to relapse\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 Continuous\cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\qr\partightenfactor0 +\cf0 \cell +\pard\intbl\itap1\pardeftab720\ri-5\sb20\sa20\partightenfactor0 +\cf0 \cell \row + +\itap1\trowd \taflags1 \trgaph108\trleft-108 \trbrdrl\brdrnil \trbrdrr\brdrnil +\clvertalt \clcbpat1 \clwWidth268\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx1440 +\clvertalt \clcbpat1 \clwWidth1373\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx2880 +\clvertalt \clcbpat1 \clwWidth2747\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx4320 +\clvertalt \clcbpat1 \clwWidth1103\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx5760 +\clvertalt \clcbpat1 \clwWidth592\clftsWidth3 \clbrdrt\brdrs\brdrw20\brdrcf2 \clbrdrl\brdrs\brdrw20\brdrcf2 \clbrdrb\brdrs\brdrw20\brdrcf2 \clbrdrr\brdrs\brdrw20\brdrcf2 \clpadt20 \clpadl100 \clpadr20 \gaph\cellx7200 +\clvertalt 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a/notebooks/Survival HTE Examples.ipynb b/notebooks/Survival HTE Examples.ipynb new file mode 100644 index 000000000..e901088ce --- /dev/null +++ b/notebooks/Survival HTE Examples.ipynb @@ -0,0 +1,652 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7ne6vz1z2yh", + "metadata": {}, + "source": [ + "# Survival HTE Examples: Heterogeneous Treatment Effects with Censored Outcomes\n", + "\n", + "This notebook demonstrates three approaches to estimating heterogeneous treatment effects (CATE) when outcomes are right-censored survival times:\n", + "\n", + "1. **Approach A — Direct survival methods**: Fit survival models on raw `(time, event)` data and compute RMST-based CATE (SurvivalTLearner, SurvivalSLearner, CausalSurvivalForest)\n", + "2. **Approach B — CUT transform + generic learners**: Transform censored outcomes into scalar pseudo-outcomes via IPCW/BJ/AIPCW, then use any standard EconML CATE estimator (TLearner, SLearner, CausalForest, UIF)\n", + "3. **Approach C — CUT transform + outcome-transform learners**: Use AIPCW pseudo-outcomes with learners that construct their own pseudo-outcomes internally (XLearner, AIPTWLearner, RLearner)\n", + "\n", + "The data generating process mirrors `original/simulation/survival/survival_hte_simulation_case1.R`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "zbq9luwu3ad", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import importlib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# EconML imports\n", + "import econml.metalearners._censor_metalearners as _censor_metalearners\n", + "_censor_metalearners = importlib.reload(_censor_metalearners)\n", + "TLearner = _censor_metalearners.TLearner\n", + "SLearner = _censor_metalearners.SLearner\n", + "XLearner = _censor_metalearners.XLearner\n", + "SurvivalTLearner = _censor_metalearners.SurvivalTLearner\n", + "SurvivalSLearner = _censor_metalearners.SurvivalSLearner\n", + "IPTWLearner = _censor_metalearners.IPTWLearner\n", + "AIPTWLearner = _censor_metalearners.AIPTWLearner\n", + "MCLearner = _censor_metalearners.MCLearner\n", + "MCEALearner = _censor_metalearners.MCEALearner\n", + "ULearner = _censor_metalearners.ULearner\n", + "RALearner = _censor_metalearners.RALearner\n", + "RLearner = _censor_metalearners.RLearner\n", + "IFLearner = _censor_metalearners.IFLearner\n", + "\n", + "import econml.grf._causal_survival_forest as _causal_survival_forest\n", + "_causal_survival_forest = importlib.reload(_causal_survival_forest)\n", + "CausalSurvivalForest = _causal_survival_forest.CausalSurvivalForest\n", + "from econml.grf import GRFCausalForest as CausalForest\n", + "from econml.censor import (\n", + " fit_nuisance_survival_crossfit,\n", + " ipcw_cut_rmst, bj_cut_rmst, aipcw_cut_rmst,\n", + " uif_diff_rmst,\n", + ")\n", + "\n", + "# DGP (ported from R simulation)\n", + "from econml.tests.test_censor.dgp import make_survival_data\n", + "\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "markdown", + "id": "h1uv5hmfu37", + "metadata": {}, + "source": [ + "## 1. Data Generating Process\n", + "\n", + "We generate survival data with:\n", + "- 6 covariates (3 correlated continuous, 3 binary)\n", + "- Logistic propensity score\n", + "- Counterfactual event times: log-logistic (a=0), log-normal (a=1)\n", + "- Treatment-dependent censoring\n", + "- Administrative censoring at t=10\n", + "- True CATE = RMST(a=1|X, tau) - RMST(a=0|X, tau) via Monte Carlo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fh6kclcsfww", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n=2000, d=6, tau=4.0\n", + "Treatment rate: 0.48\n", + "Censoring rate: 0.32\n", + "True ATE (RMST diff): -0.375\n" + ] + } + ], + "source": [ + "tau = 4.0\n", + "data = make_survival_data(n=2000, tau=tau, seed=42, compute_true_cate=True)\n", + "\n", + "X = data['X']\n", + "T = data['T']\n", + "time = data['time']\n", + "event = data['event']\n", + "true_cate = data['true_cate']\n", + "surv_obj = data['Y'] # structured array for survival learners\n", + "\n", + "clip_hte = lambda y: np.clip(np.asarray(y, dtype=float).ravel(), -2 * tau, 2 * tau)\n", + "\n", + "n = len(T)\n", + "print(f\"n={n}, d={X.shape[1]}, tau={tau}\")\n", + "print(f\"Treatment rate: {T.mean():.2f}\")\n", + "print(f\"Censoring rate: {1 - event.mean():.2f}\")\n", + "print(f\"True ATE (RMST diff): {true_cate.mean():.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c5fa818b", + "metadata": {}, + "outputs": [], + "source": [ + "# Notebook-level outer sample splitting is intentionally omitted.\n", + "# Reported CATEs come directly from each estimator's own prediction path.\n" + ] + }, + { + "cell_type": "markdown", + "id": "cltxjna6kau", + "metadata": {}, + "source": [ + "## 2. Approach A — Direct Survival Methods\n", + "\n", + "These methods take raw `(event, time)` structured arrays and internally fit survival models. No CUT transformation step needed.\n", + "\n", + "- **SurvivalTLearner / SurvivalSLearner**: Fit scikit-survival models (CoxPH) and report RMST differences from the fitted estimators\n", + "- **CausalSurvivalForest**: GRF-style honest forest with doubly-robust survival pseudo-outcomes computed internally" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0agu5qq1fp9r", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SurvivalSLearner ATE: -0.223\n", + "SurvivalTLearner ATE: -0.233\n", + "CausalSurvivalForest ATE: -0.258\n" + ] + } + ], + "source": [ + "# Survival methods: fit once on the full sample and query estimator effects\n", + "\n", + "surv_s = SurvivalSLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "surv_s.fit(surv_obj, T, X=X)\n", + "cate_surv_s = clip_hte(surv_s.effect(X))\n", + "\n", + "surv_t = SurvivalTLearner(\n", + " tau=tau,\n", + " cv=2,\n", + ")\n", + "surv_t.fit(surv_obj, T, X=X)\n", + "cate_surv_t = clip_hte(surv_t.effect(X))\n", + "\n", + "csf = CausalSurvivalForest(tau=tau, n_estimators=200, nuisance_cv=2, random_state=42)\n", + "csf.fit(X, T, time, event.astype(bool))\n", + "cate_csf = clip_hte(csf.effect(X))\n", + "\n", + "print(f\"SurvivalSLearner ATE: {cate_surv_s.mean():.3f}\")\n", + "print(f\"SurvivalTLearner ATE: {cate_surv_t.mean():.3f}\")\n", + "print(f\"CausalSurvivalForest ATE: {cate_csf.mean():.3f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c5l521h8f2h", + "metadata": {}, + "source": [ + "## 3. Approach B — CUT Transforms + Generic Learners\n", + "\n", + "Two-step pipeline:\n", + "1. **Cross-fit nuisance models** (censoring `G`, event `S`, and propensity `ps`) and **apply CUT/UIF transforms** to get scalar pseudo-outcomes\n", + "2. **Feed pseudo-outcomes** into any standard EconML learner (TLearner, SLearner, CausalForest)\n", + "\n", + "### 3a. Cross-fit nuisance models and apply transforms\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "wd9ho89md9n", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time grid: 635 points\n", + "G_a0 shape: (2000, 635)\n", + "S_a0 shape: (2000, 635)\n", + "ps range: [0.080, 0.942]\n" + ] + } + ], + "source": [ + "# Cross-fit G(t|a,X), S(t|a,X), and ps(X) using held-out folds\n", + "nuis = fit_nuisance_survival_crossfit(\n", + " time, event.astype(int), T, X,\n", + " model_cause=None,\n", + " model_competing=None,\n", + " cv=2,\n", + " random_state=42)\n", + "\n", + "print(f\"Time grid: {len(nuis.time_grid)} points\")\n", + "print(f\"G_a0 shape: {nuis.G_a0.shape}\")\n", + "print(f\"S_a0 shape: {nuis.S_a0.shape}\")\n", + "print(f\"ps range: [{nuis.ps.min():.3f}, {nuis.ps.max():.3f}]\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9wai7jua9kl", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IPCW pseudo-outcome: mean=2.425, std=2.362\n", + "BJ pseudo-outcome: mean=2.490, std=1.472\n", + "AIPCW pseudo-outcome: mean=2.472, std=1.518\n" + ] + } + ], + "source": [ + "# Apply three CUT transforms → scalar pseudo-outcomes\n", + "Y_ipcw = ipcw_cut_rmst(T, time, event, tau,\n", + " nuis.G_a0, nuis.G_a1,\n", + " time_grid=nuis.time_grid)\n", + "\n", + "Y_bj = bj_cut_rmst(T, time, event, tau,\n", + " nuis.S_a0, nuis.S_a1,\n", + " time_grid=nuis.time_grid)\n", + "\n", + "Y_aipcw = aipcw_cut_rmst(T, time, event, tau,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " time_grid=nuis.time_grid)\n", + "\n", + "print(f\"IPCW pseudo-outcome: mean={Y_ipcw.mean():.3f}, std={Y_ipcw.std():.3f}\")\n", + "print(f\"BJ pseudo-outcome: mean={Y_bj.mean():.3f}, std={Y_bj.std():.3f}\")\n", + "print(f\"AIPCW pseudo-outcome: mean={Y_aipcw.mean():.3f}, std={Y_aipcw.std():.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "qq7nrr96s9", + "metadata": {}, + "source": [ + "### 3b. Feed pseudo-outcomes into generic EconML learners\n", + "\n", + "After the CUT step, `Y_star` is a plain float array. Any existing EconML estimator works directly.\n", + "\n", + "All three transforms are available — `Y_ipcw` (singly robust, requires correct censoring model),\n", + "`Y_bj` (singly robust, requires correct event model), and `Y_aipcw` (doubly robust, recommended).\n", + "Below we use `Y_aipcw` for the learner comparisons." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ulskhjjdfj", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ATEs:\n", + " AIPCW + TLearner : -0.225\n", + " AIPCW + SLearner : -0.230\n", + " AIPCW + CausalForest : -0.233\n" + ] + } + ], + "source": [ + "# Use AIPCW pseudo-outcome (doubly robust) and fit each learner on the full sample\n", + "# Y_ipcw and Y_bj are also available as singly-robust alternatives\n", + "results = {}\n", + "\n", + "tl_est = TLearner(cv=2)\n", + "tl_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + TLearner\"] = clip_hte(tl_est.effect(X))\n", + "\n", + "sl_est = SLearner(cv=2)\n", + "sl_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + SLearner\"] = clip_hte(sl_est.effect(X))\n", + "\n", + "cf_est = CausalForest(num_trees=200, seed=42)\n", + "cf_est.fit(X, Y_aipcw, T)\n", + "results[\"AIPCW + CausalForest\"] = clip_hte(cf_est.effect(X))\n", + "\n", + "print(\"ATEs:\")\n", + "for k, v in results.items():\n", + " print(f\" {k:35s}: {v.mean():.3f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "o5392rfd6ci", + "metadata": {}, + "source": [ + "### 3c. IFLearner (Uncentered Influence Function)\n", + "\n", + "The UIF transform (`uif_diff_rmst`) produces per-subject pseudo-outcomes.\n", + "`IFLearner` regresses these scores on X — mirroring `uif_learner()` from `hte_learners.R`.\n", + "Here the balancing and tilting weights are also taken from the cross-fitted nuisance step.\n", + "The raw UIF scores (`UIF (direct)`) are also kept as a reference (population-level estimate).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "n1gms1sn7xr", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IFLearner ATE: -0.264\n", + "Raw IF ATE: -0.252\n" + ] + } + ], + "source": [ + "# UIF: use cross-fitted balancing and tilting weights\n", + "# For ATE estimation: bw = 1/(ps*a + (1-ps)*(1-a)), tilt = 1\n", + "bw = nuis.iptw\n", + "tilt = nuis.naive\n", + "\n", + "Y_uif_diff_rmst = uif_diff_rmst(T, time, event, tau, bw, tilt,\n", + " nuis.G_a0, nuis.G_a1,\n", + " nuis.S_a0, nuis.S_a1,\n", + " time_grid=nuis.time_grid)\n", + "\n", + "# IFLearner: fit on the precomputed UIF score and query estimator effects\n", + "if_est = IFLearner(cv=2)\n", + "if_est.fit(Y_uif_diff_rmst, X=X)\n", + "results[\"IFLearner\"] = clip_hte(if_est.effect(X))\n", + "\n", + "# Raw UIF scores (direct, population-level reference)\n", + "raw_if_score = Y_uif_diff_rmst\n", + "print(f\"IFLearner ATE: {results['IFLearner'].mean():.3f}\")\n", + "print(f\"Raw IF ATE: {raw_if_score.mean():.3f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "buq2184o59b", + "metadata": {}, + "source": [ + "## 4. Approach C — CUT Transforms + Outcome-Transform Learners\n", + "\n", + "These learners build their own internal pseudo-outcomes on top of the AIPCW CUT pseudo-outcome `Y*`.\n", + "\n", + "### Outcome-transform learners (XLearner, AIPTWLearner, RLearner, IPTW, MC, MCEA, U-learner)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5067nx2rk48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Approach C ATEs:\n", + " AIPCW + XLearner : -0.267\n", + " AIPCW + AIPTWLearner : -0.229\n", + " AIPCW + RLearner : -0.185\n", + " AIPCW + IPTWLearner : -0.533\n", + " AIPCW + MCLearner : -0.022\n", + " AIPCW + MCEALearner : -0.224\n", + " AIPCW + ULearner : -0.266\n", + " AIPCW + RALearner : -0.237\n" + ] + } + ], + "source": [ + "# Outcome-transform learners: fit once on the full sample and query estimator effects\n", + "xl_est = XLearner(cv=2)\n", + "xl_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + XLearner\"] = clip_hte(xl_est.effect(X))\n", + "\n", + "aiptw_est = AIPTWLearner(cv=2)\n", + "aiptw_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + AIPTWLearner\"] = clip_hte(aiptw_est.effect(X))\n", + "\n", + "r_est = RLearner(cv=2)\n", + "r_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + RLearner\"] = clip_hte(r_est.effect(X))\n", + "\n", + "iptw_est = IPTWLearner(cv=2)\n", + "iptw_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + IPTWLearner\"] = clip_hte(iptw_est.effect(X))\n", + "\n", + "mc_est = MCLearner(cv=2)\n", + "mc_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + MCLearner\"] = clip_hte(mc_est.effect(X))\n", + "\n", + "mcea_est = MCEALearner(cv=2)\n", + "mcea_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + MCEALearner\"] = clip_hte(mcea_est.effect(X))\n", + "\n", + "u_est = ULearner(cv=2)\n", + "u_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + ULearner\"] = clip_hte(u_est.effect(X))\n", + "\n", + "ra_est = RALearner(cv=2)\n", + "ra_est.fit(Y_aipcw, T, X=X)\n", + "results[\"AIPCW + RALearner\"] = clip_hte(ra_est.effect(X))\n", + "\n", + "print(\"Approach C ATEs:\")\n", + "for k in [\"AIPCW + XLearner\", \"AIPCW + AIPTWLearner\", \"AIPCW + RLearner\",\n", + " \"AIPCW + IPTWLearner\", \"AIPCW + MCLearner\",\n", + " \"AIPCW + MCEALearner\", \"AIPCW + ULearner\",\n", + " \"AIPCW + RALearner\"]:\n", + " print(f\" {k:35s}: {results[k].mean():.3f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "exuxldbsen", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "hye4imzs2d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3ku583mu7eg", + "metadata": {}, + "source": [ + "## 5. Evaluation & Comparison\n", + "\n", + "Compare all method predictions against the Monte Carlo true CATE." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "78d61kj341m", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Method ATE Bias RMSE\n", + "------------------------------------------------------------\n", + "SurvivalTLearner -0.233 0.142 0.875\n", + "SurvivalSLearner -0.223 0.152 1.021\n", + "AIPCW + TLearner -0.225 0.150 0.825\n", + "AIPCW + SLearner -0.230 0.145 1.094\n", + "AIPCW + CausalForest -0.233 0.142 0.649\n", + "IFLearner -0.264 0.111 0.721\n", + "AIPCW + XLearner -0.267 0.108 0.706\n", + "AIPCW + AIPTWLearner -0.229 0.146 0.789\n", + "AIPCW + RLearner -0.185 0.189 0.772\n", + "AIPCW + IPTWLearner -0.533 -0.159 1.784\n", + "AIPCW + MCLearner -0.022 0.353 1.812\n", + "AIPCW + MCEALearner -0.224 0.150 0.766\n", + "AIPCW + ULearner -0.266 0.109 0.962\n", + "AIPCW + RALearner -0.237 0.138 0.676\n", + "CausalSurvivalForest -0.258 0.117 0.936\n", + "True ATE -0.375\n" + ] + } + ], + "source": [ + "# Collect all learner CATE estimates\n", + "all_results = {\n", + " \"SurvivalTLearner\": cate_surv_t,\n", + " \"SurvivalSLearner\": cate_surv_s,\n", + " **results,\n", + " \"CausalSurvivalForest\": cate_csf,\n", + "}\n", + "\n", + "# Bias and RMSE table\n", + "print(\"{:35s} {:>7s} {:>7s} {:>7s}\".format(\"Method\", \"ATE\", \"Bias\", \"RMSE\"))\n", + "print(\"-\" * 60)\n", + "for name, cate_hat in all_results.items():\n", + " bias = np.mean(cate_hat - true_cate)\n", + " rmse = np.sqrt(np.mean((cate_hat - true_cate) ** 2))\n", + " ate = np.mean(cate_hat)\n", + " print(f\"{name:35s} {ate:7.3f} {bias:7.3f} {rmse:7.3f}\")\n", + "print(\"{:35s} {:7.3f}\".format(\"True ATE\", true_cate.mean()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "hsmhjlnpc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Scatter: estimated CATE vs true CATE for all learners\n", + "names = list(all_results.keys())\n", + "n_cols = 3\n", + "n_rows = int(np.ceil(len(names) / n_cols))\n", + "fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows))\n", + "axes = np.atleast_1d(axes).ravel()\n", + "\n", + "for ax, name in zip(axes, names):\n", + " cate_hat = all_results[name]\n", + " ax.scatter(true_cate, cate_hat, alpha=0.15, s=5)\n", + " lims = [min(true_cate.min(), cate_hat.min()), max(true_cate.max(), cate_hat.max())]\n", + " ax.plot(lims, lims, 'r--', lw=1)\n", + " ax.set_xlabel('True CATE')\n", + " ax.set_ylabel('Estimated CATE')\n", + " ax.set_title(name)\n", + "\n", + "for ax in axes[len(names):]:\n", + " ax.axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ngov0142yx", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Boxplot of |bias| distribution across methods\n", + "names = list(all_results.keys())\n", + "biases = [np.abs(all_results[k] - true_cate) for k in names]\n", + "\n", + "plt.figure(figsize=(14, 5))\n", + "plt.boxplot(biases, showmeans=True)\n", + "plt.xticks(range(1, len(names) + 1), names, rotation=45, ha='right')\n", + "plt.ylabel('|Bias| distribution')\n", + "plt.title('Absolute bias distribution across CATE estimators')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "hte-boxplot-estimates", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Boxplot of estimated HTE distributions across methods\n", + "names = list(all_results.keys())\n", + "hte_values = [np.asarray(all_results[k]).ravel() for k in names]\n", + "\n", + "plt.figure(figsize=(14, 5))\n", + "plt.boxplot(hte_values, showmeans=True)\n", + "plt.xticks(range(1, len(names) + 1), names, rotation=45, ha='right')\n", + "plt.ylabel('Estimated HTE')\n", + "plt.title('Estimated HTE distribution across CATE estimators')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}