diff --git a/NAMESPACE b/NAMESPACE
index 9c7147e5..52a83da7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -48,6 +48,7 @@ export(style_roche_number)
 export(style_roche_percent)
 export(style_roche_pvalue)
 export(style_roche_ratio)
+export(tbl_ancova)
 export(tbl_baseline_chg)
 export(tbl_coxph)
 export(tbl_demographics)
diff --git a/NEWS.md b/NEWS.md
index 2122ed67..60823d43 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,8 @@
 
 ## New Functions and Functionality
 
+* Added `tbl_ancova()` for building ANCOVA tables with adjusted means and treatment-vs-control contrasts using `emmeans`. (#6)
+
 * Added `g_lineplot()` for creating line plots with optional summary statistics table. Includes helper functions `calc_stats()`, `g_lineplot_table()`, and `preprocess_lineplot_data()`.
 
 * Added `ard_tabulate_abnormal_by_baseline()` for creating an Analysis Results Data (ARD) object counting participants with abnormal assessments, stratified by their baseline status.
diff --git a/R/tbl_ancova.R b/R/tbl_ancova.R
new file mode 100644
index 00000000..c50dd67d
--- /dev/null
+++ b/R/tbl_ancova.R
@@ -0,0 +1,372 @@
+#' ANCOVA Table
+#'
+#' @description
+#' Builds a table of adjusted means and treatment-vs-control contrasts
+#' from a linear model (ANCOVA). The table displays, for each treatment group:
+#' - n (number of observations)
+#' - Adjusted Mean (least-squares mean)
+#' - Difference in Adjusted Means (vs reference group)
+#' - Confidence interval for the difference
+#' - p-value for the difference
+#'
+#' The model is fit via [`cardx::construct_model()`] and least-squares
+#' estimates are obtained with the `emmeans` package. Contrasts use
+#' `emmeans::contrast(method = "trt.vs.ctrl")` so that each non-reference
+#' group is compared to the reference group only.
+#'
+#' @param data (`data.frame`)\cr
+#'   Analysis data set, typically one parameter/visit subset.
+#' @param formula (`formula`)\cr
+#'   Model formula passed to the fitting function. The left-hand side is the
+#'   response (e.g. `CHG`), the right-hand side includes the treatment variable
+#'   and any covariates (e.g. `CHG ~ TRT01A + BASE`).
+#' @param by ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
+#'   Column in `data` identifying treatment groups. This variable must also
+#'   appear on the right-hand side of `formula`. Used for column headers.
+#' @param ref_group (`string`)\cr
+#'   Level of `by` that is the reference (control) group. Contrasts are
+#'   computed as each non-reference group minus this group.
+#' @param conf.level (scalar `numeric`)\cr
+#'   Confidence level for the difference in adjusted means.
+#'   Default is `0.95`.
+#' @param method (`string`)\cr
+#'   Modelling function name. Default is `"lm"`.
+#' @param method.args (`list`)\cr
+#'   Additional arguments passed to the modelling function.
+#' @param package (`string`)\cr
+#'   Package exporting `method`. Default is `"stats"`.
+#' @param adjust (`string`)\cr
+#'   Multiplicity adjustment method for contrasts, passed to
+#'   [`emmeans::contrast()`]. Common choices: `"none"` (no adjustment,
+#'   default), `"dunnett"` (Dunnett's method), `"bonferroni"`, `"tukey"`.
+#'   See [`emmeans::summary.emmGrid()`] for all options.
+#' @param label (`string`)\cr
+#'   Label for the top-level row in the table. Defaults to the label attribute
+#'   of the response variable, or the variable name if no label exists.
+#'   When used inside `tbl_strata(strata = PARAM)`, pass the parameter value
+#'   (e.g. `unique(data$PARAM)`) so each sub-table is labelled correctly.
+#' @param denominator (`data.frame`)\cr
+#'   Optional data frame used to compute the header Ns (typically `ADSL`).
+#'   When supplied, the column headers show `(N = <count>)` from this data
+#'   frame rather than from `data`.
+#'
+#' @return A `'gtsummary'` table of class `c("tbl_ancova", "gtsummary")`.
+#' @name tbl_ancova
+#'
+#' @examples
+#' # Simple ANCOVA with baseline covariate
+#' cards::ADLB |>
+#'   dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+#'   tbl_ancova(
+#'     formula = CHG ~ TRTA + BASE,
+#'     by = TRTA,
+#'     ref_group = "Placebo"
+#'   )
+#'
+#' # With denominator for header Ns
+#' cards::ADLB |>
+#'   dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+#'   tbl_ancova(
+#'     formula = CHG ~ TRTA + BASE,
+#'     by = TRTA,
+#'     ref_group = "Placebo",
+#'     denominator = cards::ADSL
+#'   )
+#'
+#' # With custom label (e.g. parameter name inside tbl_strata)
+#' cards::ADLB |>
+#'   dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+#'   tbl_ancova(
+#'     formula = CHG ~ TRTA + BASE,
+#'     by = TRTA,
+#'     ref_group = "Placebo",
+#'     label = "Sodium (mmol/L)"
+#'   )
+#'
+#' # With Dunnett's multiplicity adjustment
+#' cards::ADLB |>
+#'   dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+#'   tbl_ancova(
+#'     formula = CHG ~ TRTA + BASE,
+#'     by = TRTA,
+#'     ref_group = "Placebo",
+#'     adjust = "dunnett"
+#'   )
+#'
+#' # Custom column order — set factor levels on the input data
+#' cards::ADLB |>
+#'   dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+#'   dplyr::mutate(TRTA = factor(TRTA, levels = c(
+#'     "Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"
+#'   ))) |>
+#'   tbl_ancova(
+#'     formula = CHG ~ TRTA + BASE,
+#'     by = TRTA,
+#'     ref_group = "Placebo"
+#'   )
+#'
+#' @export
+tbl_ancova <- function(data,
+                       formula,
+                       by,
+                       ref_group,
+                       conf.level = 0.95,
+                       method = "lm",
+                       method.args = list(),
+                       package = "stats",
+                       adjust = "none",
+                       label = NULL,
+                       denominator = NULL) {
+  set_cli_abort_call()
+
+  # input checks ---------------------------------------------------------------
+  check_not_missing(data)
+  check_not_missing(formula)
+  check_not_missing(ref_group)
+
+  check_class(data, "data.frame")
+  check_class(formula, "formula")
+  check_string(ref_group)
+  check_string(method)
+  check_string(package)
+  check_scalar(conf.level)
+  check_range(conf.level, range = c(0, 1))
+  check_string(adjust)
+  if (!is.null(label)) check_string(label)
+  check_pkg_installed("emmeans")
+
+  by <- dplyr::select(data, {{ by }}) |> names()
+  check_scalar(by, allow_empty = FALSE)
+
+  if (!ref_group %in% unique(data[[by]])) {
+    cli::cli_abort(
+      "{.arg ref_group} value {.val {ref_group}} not found in column {.val {by}}.",
+      call = get_cli_abort_call()
+    )
+  }
+
+  # relevel so ref_group is first <U+2014> emmeans uses the first level as reference
+  data[[by]] <- stats::relevel(factor(data[[by]]), ref = ref_group)
+
+  # fit model and compute emmeans ----------------------------------------------
+  mod <- cardx::construct_model(
+    data = data, formula = formula, method = method,
+    method.args = {{ method.args }}, package = package
+  )
+
+  emmeans_specs <- stats::reformulate(by)
+  emm <- emmeans::emmeans(mod, specs = emmeans_specs)
+
+  # LS means -------------------------------------------------------------------
+  emm_summary <- summary(emm, calc = c(n = ".wgt.")) |>
+    dplyr::as_tibble() |>
+    dplyr::rename(
+      estimate = dplyr::any_of(c("emmean", "prob")),
+      n = dplyr::any_of("n")
+    )
+
+  # contrasts (treatment vs control) -------------------------------------------
+  contr_summary <- emmeans::contrast(emm, method = "trt.vs.ctrl", ref = ref_group, adjust = adjust) |>
+    summary(infer = TRUE, level = conf.level) |>
+    dplyr::as_tibble() |>
+    dplyr::rename(
+      conf.low = dplyr::any_of(c("lower.CL", "asymp.LCL")),
+      conf.high = dplyr::any_of(c("upper.CL", "asymp.UCL"))
+    )
+
+
+  # parse contrast labels to extract the non-reference group name
+  # e.g. "Xanomeline High Dose - Placebo" -> "Xanomeline High Dose"
+  contr_summary$trt_group <- sub(
+    paste0("\\s*-\\s*", .escape_regex(ref_group), "$"), "",
+    contr_summary$contrast
+  )
+
+  # build ARD for tbl_ard_summary ----------------------------------------------
+  trt_levels <- levels(data[[by]])
+  endpoint_var <- all.vars(formula)[1]
+  endpoint_label <- label %||% attr(data[[endpoint_var]], "label") %||% endpoint_var
+
+  ard <- .build_ancova_ard(
+    emm_summary = emm_summary,
+    contr_summary = contr_summary,
+    by = by,
+    trt_levels = trt_levels,
+    ref_group = ref_group,
+    endpoint_label = endpoint_label,
+    conf.level = conf.level
+  )
+
+  # build gtsummary table ------------------------------------------------------
+  ci_pct <- paste0(round(conf.level * 100), "%")
+
+  tbl <- gtsummary::tbl_ard_summary(
+    cards = ard,
+    by = dplyr::all_of(by),
+    type = everything() ~ "continuous2",
+    statistic = everything() ~ c(
+      "{n}",
+      "{estimate}",
+      "{mean.diff}",
+      paste0("{conf.low}, {conf.high}"),
+      "{p.value}"
+    ),
+  ) |>
+    gtsummary::modify_table_body(
+      ~ .x |>
+        dplyr::mutate(
+          label = dplyr::case_when(
+            .data$label == "n" ~ "n",
+            .data$label == "Mean" ~ "Adjusted Mean",
+            .data$label == "Mean Difference" ~ "Difference in Adjusted Means",
+            .data$label == "CI Lower Bound, CI Upper Bound" ~
+              paste0(ci_pct, " CI for Difference in Adjusted Means"),
+            .data$label == "p-value" ~ "p-value",
+            TRUE ~ .data$label
+          ),
+          # blank out stats that don't apply to the reference group
+          dplyr::across(
+            dplyr::starts_with("stat_"),
+            ~ dplyr::if_else(
+              .data$label %in% c(
+                "Difference in Adjusted Means",
+                paste0(ci_pct, " CI for Difference in Adjusted Means"),
+                "p-value"
+              ) & grepl("NA", .x, fixed = TRUE),
+              "",
+              .x
+            )
+          )
+        )
+    )
+
+  # add denominator header Ns if provided
+  if (!is.null(denominator)) {
+    check_class(denominator, "data.frame")
+    if (by %in% names(denominator)) {
+      # read the column-to-group mapping that tbl_ard_summary assigned
+      col_map <- tbl$table_body |>
+        dplyr::select(dplyr::starts_with("stat_")) |>
+        names()
+      # extract group labels from existing headers (format: "**Group**")
+      existing_headers <- tbl$table_styling$header |>
+        dplyr::filter(.data$column %in% col_map) |>
+        dplyr::mutate(group = gsub("^\\*\\*|\\*\\*$", "", .data$label)) |>
+        dplyr::select("column", "group")
+
+      denom_n <- denominator |>
+        dplyr::count(!!rlang::sym(by), name = "n")
+
+      header_df <- existing_headers |>
+        dplyr::left_join(denom_n, by = stats::setNames(by, "group"))
+
+      header_labels <- stats::setNames(
+        paste0("**", header_df$group, "**\n(N = ", header_df$n, ")"),
+        header_df$column
+      )
+      tbl <- tbl |>
+        gtsummary::modify_header(!!!header_labels)
+    }
+  }
+
+  tbl |>
+    structure(
+      class = c("tbl_ancova", class(tbl)),
+      by = by,
+      ref_group = ref_group,
+      conf.level = conf.level,
+      adjust = adjust,
+      method = method,
+      package = package
+    )
+}
+
+
+# Assembles an ARD (Analysis Results Dataset) from emmeans summaries.
+# For each treatment level, creates rows for: n, adjusted mean (from emm_summary),
+# and contrast stats mean difference, CI bounds, p-value (from contr_summary).
+# Reference group gets NA for contrast stats so the table structure is uniform.
+.build_ancova_ard <- function(emm_summary, contr_summary, by,
+                              trt_levels, ref_group, endpoint_label,
+                              conf.level) {
+  ard_rows <- list()
+
+  for (trt in trt_levels) {
+    emm_row <- emm_summary[emm_summary[[by]] == trt, ]
+    n_val <- as.integer(emm_row$n)
+    est_val <- emm_row$estimate
+
+    # base rows: n and adjusted mean
+    ard_rows <- c(ard_rows, list(
+      .ancova_ard_row(endpoint_label, by, trt, "n", n_val, "n", "integer"),
+      .ancova_ard_row(endpoint_label, by, trt, "estimate", est_val, "Mean", "numeric")
+    ))
+
+    # contrast rows (only for non-reference groups)
+    if (trt != ref_group) {
+      contr_row <- contr_summary[contr_summary$trt_group == trt, ]
+      if (nrow(contr_row) == 1) {
+        ard_rows <- c(ard_rows, list(
+          .ancova_ard_row(endpoint_label, by, trt, "mean.diff", contr_row$estimate, "Mean Difference", "numeric"),
+          .ancova_ard_row(endpoint_label, by, trt, "conf.low", contr_row$conf.low, "CI Lower Bound", "numeric"),
+          .ancova_ard_row(endpoint_label, by, trt, "conf.high", contr_row$conf.high, "CI Upper Bound", "numeric"),
+          .ancova_ard_row(endpoint_label, by, trt, "p.value", contr_row$p.value, "p-value", "numeric")
+        ))
+      }
+    } else {
+      # reference group: fill with NA so the table structure is consistent
+      ard_rows <- c(ard_rows, list(
+        .ancova_ard_row(endpoint_label, by, trt, "mean.diff", NA_real_, "Mean Difference", "numeric"),
+        .ancova_ard_row(endpoint_label, by, trt, "conf.low", NA_real_, "CI Lower Bound", "numeric"),
+        .ancova_ard_row(endpoint_label, by, trt, "conf.high", NA_real_, "CI Upper Bound", "numeric"),
+        .ancova_ard_row(endpoint_label, by, trt, "p.value", NA_real_, "p-value", "numeric")
+      ))
+    }
+  }
+
+  dplyr::bind_rows(ard_rows) |>
+    dplyr::mutate(
+      context = "continuous",
+      fmt_fun = map(.data$stat_name, function(nm) {
+        switch(nm,
+          "n" = function(x) sprintf("%.0f", x),
+          "estimate" = ,
+          "mean.diff" = ,
+          "conf.low" = ,
+          "conf.high" = function(x) sub("^-0.00$", "0.00", sprintf("%.2f", x)),
+          "p.value" = function(x) sprintf("%.4f", x),
+          function(x) as.character(x)
+        )
+      })
+    ) |>
+    cards::as_card()
+}
+
+
+# Internal: create a single ARD row --------------------------------------------
+.ancova_ard_row <- function(variable, group1, group1_level,
+                            stat_name, stat, stat_label, var_type) {
+  dplyr::tibble(
+    variable = variable,
+    var_type = var_type,
+    var_label = variable,
+    group1 = group1,
+    group1_level = list(group1_level),
+    stat_name = stat_name,
+    stat_label = stat_label,
+    stat = list(stat),
+    warning = list(NULL),
+    error = list(NULL)
+  )
+}
+
+
+# Escapes regex special characters so group names can be used in sub() patterns.
+# e.g. "Arm (High)" -> "Arm \\(High\\)", "Dose 1.5mg" -> "Dose 1\\.5mg"
+.escape_regex <- function(x) {
+  chars <- c(".", "|", "^", "$", "(", ")", "[", "]", "{", "}", "*", "+", "?", "\\")
+  for (ch in chars) {
+    x <- gsub(ch, paste0("\\", ch), x, fixed = TRUE)
+  }
+  x
+}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index caa3260f..e59599fa 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -23,6 +23,10 @@ reference:
       - add_hierarchical_count_row
       - ard_tabulate_abnormal_by_baseline
 
+  - title: "ANCOVA Tables"
+    contents:
+      - tbl_ancova
+
   - title: "Survival Analysis Tables"
     contents:
       - tbl_survfit_quantiles
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 0a17e3c7..2e94b4b5 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -3,13 +3,16 @@ ADSL
 ADaM
 AE
 AEs
+ANCOVA
 ARD
 ARDs
+Dunnett's
 GDSR
 HLT
 Hoffmann
 Kaplan
 MMRM
+Modelling
 Ns
 ORCID
 PY
@@ -18,6 +21,7 @@ Pharmacokinetics
 RMP
 RMPT
 RStudio
+Recode
 Rua
 SOCs
 Tidyverse
@@ -27,6 +31,7 @@ cardx
 custiomization
 customizations
 de
+durations
 efron
 fleming
 flextable
@@ -35,14 +40,18 @@ gehan
 ggplot
 gtsummary
 harrington
+jitter
+labelled
 lineplot
 lineplots
+modelling
 peto
 pharma
 pre
 prentice
 quosure
 quosures
+recodes
 responder
 rlang's
 survfit
diff --git a/man/tbl_ancova.Rd b/man/tbl_ancova.Rd
new file mode 100644
index 00000000..c76857e7
--- /dev/null
+++ b/man/tbl_ancova.Rd
@@ -0,0 +1,139 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tbl_ancova.R
+\name{tbl_ancova}
+\alias{tbl_ancova}
+\title{ANCOVA Table}
+\usage{
+tbl_ancova(
+  data,
+  formula,
+  by,
+  ref_group,
+  conf.level = 0.95,
+  method = "lm",
+  method.args = list(),
+  package = "stats",
+  adjust = "none",
+  label = NULL,
+  denominator = NULL
+)
+}
+\arguments{
+\item{data}{(\code{data.frame})\cr
+Analysis data set, typically one parameter/visit subset.}
+
+\item{formula}{(\code{formula})\cr
+Model formula passed to the fitting function. The left-hand side is the
+response (e.g. \code{CHG}), the right-hand side includes the treatment variable
+and any covariates (e.g. \code{CHG ~ TRT01A + BASE}).}
+
+\item{by}{(\code{\link[dplyr:dplyr_tidy_select]{tidy-select}})\cr
+Column in \code{data} identifying treatment groups. This variable must also
+appear on the right-hand side of \code{formula}. Used for column headers.}
+
+\item{ref_group}{(\code{string})\cr
+Level of \code{by} that is the reference (control) group. Contrasts are
+computed as each non-reference group minus this group.}
+
+\item{conf.level}{(scalar \code{numeric})\cr
+Confidence level for the difference in adjusted means.
+Default is \code{0.95}.}
+
+\item{method}{(\code{string})\cr
+Modelling function name. Default is \code{"lm"}.}
+
+\item{method.args}{(\code{list})\cr
+Additional arguments passed to the modelling function.}
+
+\item{package}{(\code{string})\cr
+Package exporting \code{method}. Default is \code{"stats"}.}
+
+\item{adjust}{(\code{string})\cr
+Multiplicity adjustment method for contrasts, passed to
+\code{\link[emmeans:contrast]{emmeans::contrast()}}. Common choices: \code{"none"} (no adjustment,
+default), \code{"dunnett"} (Dunnett's method), \code{"bonferroni"}, \code{"tukey"}.
+See \code{\link[emmeans:summary.emmGrid]{emmeans::summary.emmGrid()}} for all options.}
+
+\item{label}{(\code{string})\cr
+Label for the top-level row in the table. Defaults to the label attribute
+of the response variable, or the variable name if no label exists.
+When used inside \code{tbl_strata(strata = PARAM)}, pass the parameter value
+(e.g. \code{unique(data$PARAM)}) so each sub-table is labelled correctly.}
+
+\item{denominator}{(\code{data.frame})\cr
+Optional data frame used to compute the header Ns (typically \code{ADSL}).
+When supplied, the column headers show \verb{(N = <count>)} from this data
+frame rather than from \code{data}.}
+}
+\value{
+A \code{'gtsummary'} table of class \code{c("tbl_ancova", "gtsummary")}.
+}
+\description{
+Builds a table of adjusted means and treatment-vs-control contrasts
+from a linear model (ANCOVA). The table displays, for each treatment group:
+\itemize{
+\item n (number of observations)
+\item Adjusted Mean (least-squares mean)
+\item Difference in Adjusted Means (vs reference group)
+\item Confidence interval for the difference
+\item p-value for the difference
+}
+
+The model is fit via \code{\link[cardx:construct_model]{cardx::construct_model()}} and least-squares
+estimates are obtained with the \code{emmeans} package. Contrasts use
+\code{emmeans::contrast(method = "trt.vs.ctrl")} so that each non-reference
+group is compared to the reference group only.
+}
+\examples{
+# Simple ANCOVA with baseline covariate
+cards::ADLB |>
+  dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+  tbl_ancova(
+    formula = CHG ~ TRTA + BASE,
+    by = TRTA,
+    ref_group = "Placebo"
+  )
+
+# With denominator for header Ns
+cards::ADLB |>
+  dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+  tbl_ancova(
+    formula = CHG ~ TRTA + BASE,
+    by = TRTA,
+    ref_group = "Placebo",
+    denominator = cards::ADSL
+  )
+
+# With custom label (e.g. parameter name inside tbl_strata)
+cards::ADLB |>
+  dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+  tbl_ancova(
+    formula = CHG ~ TRTA + BASE,
+    by = TRTA,
+    ref_group = "Placebo",
+    label = "Sodium (mmol/L)"
+  )
+
+# With Dunnett's multiplicity adjustment
+cards::ADLB |>
+  dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+  tbl_ancova(
+    formula = CHG ~ TRTA + BASE,
+    by = TRTA,
+    ref_group = "Placebo",
+    adjust = "dunnett"
+  )
+
+# Custom column order — set factor levels on the input data
+cards::ADLB |>
+  dplyr::filter(PARAMCD == "SODIUM", AVISIT == "Week 8") |>
+  dplyr::mutate(TRTA = factor(TRTA, levels = c(
+    "Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"
+  ))) |>
+  tbl_ancova(
+    formula = CHG ~ TRTA + BASE,
+    by = TRTA,
+    ref_group = "Placebo"
+  )
+
+}
diff --git a/tests/testthat/_snaps/tbl_ancova.md b/tests/testthat/_snaps/tbl_ancova.md
new file mode 100644
index 00000000..0e86e37f
--- /dev/null
+++ b/tests/testthat/_snaps/tbl_ancova.md
@@ -0,0 +1,82 @@
+# tbl_ancova() works with default settings
+
+    Code
+      as.data.frame(tbl)
+    Output
+                                 Characteristic Placebo Xanomeline High Dose Xanomeline Low Dose
+      1                    Change from Baseline    <NA>                 <NA>                <NA>
+      2                                       n       5                    4                   3
+      3                           Adjusted Mean    0.60                -0.66               -0.47
+      4            Difference in Adjusted Means                        -1.26               -1.07
+      5 95% CI for Difference in Adjusted Means                  -4.23, 1.71         -4.35, 2.21
+      6                                 p-value                       0.3563              0.4733
+
+# tbl_ancova() works with denominator
+
+    Code
+      as.data.frame(tbl)
+    Output
+                                 Characteristic Placebo\n(N = 86) Xanomeline High Dose\n(N = 84) Xanomeline Low Dose\n(N = 84)
+      1                    Change from Baseline              <NA>                           <NA>                          <NA>
+      2                                       n                 5                              4                             3
+      3                           Adjusted Mean              0.60                          -0.66                         -0.47
+      4            Difference in Adjusted Means                                            -1.26                         -1.07
+      5 95% CI for Difference in Adjusted Means                                      -4.23, 1.71                   -4.35, 2.21
+      6                                 p-value                                           0.3563                        0.4733
+
+# tbl_ancova() errors on invalid ref_group
+
+    Code
+      tbl_ancova(data = df_ancova, formula = CHG ~ TRTA + BASE, by = TRTA, ref_group = "NonexistentArm")
+    Condition
+      Error in `tbl_ancova()`:
+      ! `ref_group` value "NonexistentArm" not found in column "TRTA".
+
+# tbl_ancova() works with custom label
+
+    Code
+      as.data.frame(tbl)
+    Output
+                                 Characteristic Placebo Xanomeline High Dose Xanomeline Low Dose
+      1                         Sodium (mmol/L)    <NA>                 <NA>                <NA>
+      2                                       n       5                    4                   3
+      3                           Adjusted Mean    0.60                -0.66               -0.47
+      4            Difference in Adjusted Means                        -1.26               -1.07
+      5 95% CI for Difference in Adjusted Means                  -4.23, 1.71         -4.35, 2.21
+      6                                 p-value                       0.3563              0.4733
+
+# tbl_ancova() errors on non-string label
+
+    Code
+      tbl_ancova(data = df_ancova, formula = CHG ~ TRTA + BASE, by = TRTA, ref_group = "Placebo",
+      label = 123)
+    Condition
+      Error in `tbl_ancova()`:
+      ! The `label` argument must be a string, not a number.
+
+# tbl_ancova() works with Dunnett adjustment
+
+    Code
+      as.data.frame(tbl)
+    Output
+                                 Characteristic Placebo Xanomeline High Dose Xanomeline Low Dose
+      1                    Change from Baseline    <NA>                 <NA>                <NA>
+      2                                       n       5                    4                   3
+      3                           Adjusted Mean    0.60                -0.66               -0.47
+      4            Difference in Adjusted Means                        -1.26               -1.07
+      5 95% CI for Difference in Adjusted Means                  -4.73, 2.22         -4.91, 2.77
+      6                                 p-value                       0.5491              0.6839
+
+# tbl_ancova() works without covariates
+
+    Code
+      as.data.frame(tbl)
+    Output
+                                 Characteristic Placebo Xanomeline High Dose Xanomeline Low Dose
+      1                    Change from Baseline    <NA>                 <NA>                <NA>
+      2                                       n       5                    4                   3
+      3                           Adjusted Mean    0.20                -0.50                0.00
+      4            Difference in Adjusted Means                        -0.70               -0.20
+      5 95% CI for Difference in Adjusted Means                  -4.34, 2.94         -4.16, 3.76
+      6                                 p-value                       0.6738              0.9116
+
diff --git a/tests/testthat/test-tbl_ancova.R b/tests/testthat/test-tbl_ancova.R
new file mode 100644
index 00000000..9e43ce1f
--- /dev/null
+++ b/tests/testthat/test-tbl_ancova.R
@@ -0,0 +1,232 @@
+df_ancova <- cards::ADLB |>
+  dplyr::filter(
+    PARAMCD == "SODIUM",
+    AVISIT == "Week 8"
+  ) |>
+  dplyr::mutate(TRTA = factor(TRTA))
+
+
+test_that("tbl_ancova() works with default settings", {
+  withr::local_options(list(width = 200))
+
+  expect_silent(
+    tbl <- tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA + BASE,
+      by = TRTA,
+      ref_group = "Placebo"
+    )
+  )
+
+  expect_s3_class(tbl, "tbl_ancova")
+  expect_s3_class(tbl, "gtsummary")
+
+  # table has the expected row labels
+  labels <- tbl$table_body$label[tbl$table_body$row_type == "level"]
+  expect_true("n" %in% labels)
+  expect_true("Adjusted Mean" %in% labels)
+  expect_true("Difference in Adjusted Means" %in% labels)
+  expect_true("p-value" %in% labels)
+
+  # reference group has blank contrast cells
+  ref_col <- tbl$table_body |>
+    dplyr::filter(.data$label == "Difference in Adjusted Means") |>
+    dplyr::pull("stat_1")
+  expect_equal(ref_col, "")
+
+  expect_snapshot(as.data.frame(tbl))
+})
+
+
+test_that("tbl_ancova() works with denominator", {
+  withr::local_options(list(width = 200))
+
+  expect_silent(
+    tbl <- tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA + BASE,
+      by = TRTA,
+      ref_group = "Placebo",
+      denominator = cards::ADSL
+    )
+  )
+
+  expect_s3_class(tbl, "tbl_ancova")
+
+  # header Ns come from ADSL, not from the analysis data
+  adsl_n <- cards::ADSL |>
+    dplyr::count(TRTA)
+  headers <- tbl$table_styling$header |>
+    dplyr::filter(grepl("^stat_", .data$column))
+  for (i in seq_len(nrow(adsl_n))) {
+    matching_header <- headers$label[grepl(adsl_n$TRTA[i], headers$label, fixed = TRUE)]
+    expect_true(
+      length(matching_header) == 1 && grepl(paste0("N = ", adsl_n$n[i]), matching_header),
+      label = paste("Header for", adsl_n$TRTA[i], "should show N =", adsl_n$n[i])
+    )
+  }
+
+  expect_snapshot(as.data.frame(tbl))
+})
+
+
+test_that("tbl_ancova() denominator headers align when ref_group is not alphabetically first", {
+  withr::local_options(list(width = 200))
+
+  # Arms where the reference group ("B: Placebo") sorts after "A: Drug X"
+  df_alpha <- df_ancova |>
+    dplyr::mutate(ARM = dplyr::case_when(
+      TRTA == "Placebo" ~ "B: Placebo",
+      TRTA == "Xanomeline High Dose" ~ "A: Drug X",
+      TRTA == "Xanomeline Low Dose" ~ "C: Combination"
+    ))
+  adsl_alpha <- cards::ADSL |>
+    dplyr::mutate(ARM = dplyr::case_when(
+      TRTA == "Placebo" ~ "B: Placebo",
+      TRTA == "Xanomeline High Dose" ~ "A: Drug X",
+      TRTA == "Xanomeline Low Dose" ~ "C: Combination"
+    ))
+
+  expect_silent(
+    tbl <- tbl_ancova(
+      data = df_alpha,
+      formula = CHG ~ ARM + BASE,
+      by = ARM,
+      ref_group = "B: Placebo",
+      denominator = adsl_alpha
+    )
+  )
+
+  # verify each column header matches its data:
+  # the reference group column must show blank contrast rows
+  headers <- tbl$table_styling$header |>
+    dplyr::filter(grepl("^stat_", .data$column))
+  ref_col <- headers$column[grepl("B: Placebo", headers$label)]
+
+  diff_row <- tbl$table_body |>
+    dplyr::filter(.data$label == "Difference in Adjusted Means")
+  # reference column must be blank
+
+  expect_equal(diff_row[[ref_col]], "")
+  # non-reference columns must have numeric values
+  non_ref_cols <- setdiff(headers$column, ref_col)
+  for (col in non_ref_cols) {
+    expect_false(diff_row[[col]] == "", label = paste("column", col, "should not be blank"))
+  }
+})
+
+test_that("tbl_ancova() works with conf.level = 0.90", {
+  withr::local_options(list(width = 200))
+
+  expect_silent(
+    tbl <- tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA + BASE,
+      by = TRTA,
+      ref_group = "Placebo",
+      conf.level = 0.90
+    )
+  )
+
+  # CI label reflects 90%
+  labels <- tbl$table_body$label[tbl$table_body$row_type == "level"]
+  expect_true(any(grepl("90%", labels)))
+})
+
+
+test_that("tbl_ancova() errors on invalid ref_group", {
+  expect_snapshot(
+    tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA + BASE,
+      by = TRTA,
+      ref_group = "NonexistentArm"
+    ),
+    error = TRUE
+  )
+})
+
+
+test_that("tbl_ancova() works with custom label", {
+  withr::local_options(list(width = 200))
+
+  expect_silent(
+    tbl <- tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA + BASE,
+      by = TRTA,
+      ref_group = "Placebo",
+      label = "Sodium (mmol/L)"
+    )
+  )
+
+  expect_s3_class(tbl, "tbl_ancova")
+
+  # the custom label appears in the table body instead of the variable name
+  top_label <- tbl$table_body$label[tbl$table_body$row_type == "label"]
+  expect_true("Sodium (mmol/L)" %in% top_label)
+  expect_false("CHG" %in% top_label)
+  expect_false("Change from Baseline" %in% top_label)
+
+  expect_snapshot(as.data.frame(tbl))
+})
+
+test_that("tbl_ancova() errors on non-string label", {
+  expect_snapshot(
+    tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA + BASE,
+      by = TRTA,
+      ref_group = "Placebo",
+      label = 123
+    ),
+    error = TRUE
+  )
+})
+
+
+test_that("tbl_ancova() works with Dunnett adjustment", {
+  withr::local_options(list(width = 200))
+
+  expect_silent(
+    tbl <- tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA + BASE,
+      by = TRTA,
+      ref_group = "Placebo",
+      adjust = "dunnett"
+    )
+  )
+
+  expect_s3_class(tbl, "tbl_ancova")
+  expect_equal(attr(tbl, "adjust"), "dunnett")
+  expect_equal(attr(tbl, "method"), "lm")
+  expect_equal(attr(tbl, "package"), "stats")
+  expect_equal(attr(tbl, "by"), "TRTA")
+  expect_equal(attr(tbl, "ref_group"), "Placebo")
+
+  expect_snapshot(as.data.frame(tbl))
+})
+
+
+test_that("tbl_ancova() works without covariates", {
+  withr::local_options(list(width = 200))
+
+  expect_silent(
+    tbl <- tbl_ancova(
+      data = df_ancova,
+      formula = CHG ~ TRTA,
+      by = TRTA,
+      ref_group = "Placebo"
+    )
+  )
+
+  expect_s3_class(tbl, "tbl_ancova")
+
+  # verify labels are present even without covariates
+  labels <- tbl$table_body$label[tbl$table_body$row_type == "level"]
+  expect_true("Adjusted Mean" %in% labels)
+  expect_true("Difference in Adjusted Means" %in% labels)
+
+  expect_snapshot(as.data.frame(tbl))
+})