PhD Statistical Consultant | Advanced Quantitative Modeling (R, Mplus)
I help researchers, academic authors, doctoral candidates, and organizations turn complex data into statistically sound, interpretable, and publication-ready results.
My work focuses on selecting defensible analytical strategies, evaluating assumptions carefully, and producing results that can hold under peer review, supervisor review, or stakeholder scrutiny.
- Latent Profile Analysis (LPA) and Latent Class Analysis (LCA)
- Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)
- Mixture modeling, covariates, and distal outcomes
- Causal inference and observational-study interpretation
- Clinical, real-world evidence (RWE), and health-research modeling
- HEOR/HTA, cost-effectiveness analysis, and uncertainty-based decision modeling
- Econometrics, simulation, and statistical validation
- Statistical tools: R, Mplus, Python, Stata, SPSS
- Applied strengths: Reproducible statistical computing, interpretable predictive modeling, validation, and simulation-based decision analysis
A reproducible clinical/RWE predictive-modeling workflow emphasizing patient-level leakage prevention, protected test-set evaluation, benchmark comparison, probability calibration, and clinically cautious risk interpretation.
A transparent HEOR/HTA decision-modeling workflow comparing two strategies through expected costs and QALYs, incremental analysis, net monetary benefit, Monte Carlo probabilistic sensitivity analysis, a cost-effectiveness plane, a CEAC, scenario analysis, and reproducible exports.