diff --git a/NAMESPACE b/NAMESPACE
index 5129b999..d7432de8 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -26,6 +26,7 @@ export(ard_car_anova)
 export(ard_car_vif)
 export(ard_categorical)
 export(ard_categorical_ci)
+export(ard_categorical_max)
 export(ard_continuous)
 export(ard_continuous_ci)
 export(ard_dichotomous)
diff --git a/NEWS.md b/NEWS.md
index 928024a0..a59c0f33 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -6,6 +6,8 @@
 
 * Fixed a bug in `ard_survival_survfit()` causing an error when "=" character is present in stratification variable level labels. (#252)
 
+* Added function `ard_categorical_max()` to calculate categorical occurrence rates by maximum level per unique ID. (#240)
+
 # cardx 0.2.2
 
 * Added a `data.frame` method to `ard_survival_survfit()`.
diff --git a/R/ard_categorical_max.R b/R/ard_categorical_max.R
new file mode 100644
index 00000000..659386bd
--- /dev/null
+++ b/R/ard_categorical_max.R
@@ -0,0 +1,108 @@
+#' ARD to Calculate Categorical Occurrence Rates by Maximum Level Per Unique ID
+#'
+#' Function calculates categorical variable level occurrences rates by maximum level per unique ID.
+#' Each variable in `variables` is evaluated independently and then results for all variables are stacked.
+#' Only the highest-ordered level will be counted for each unique ID.
+#' Unordered, non-numeric variables will be converted to factor and the default level order used for ordering.
+#'
+#' @inheritParams cards::ard_categorical
+#' @inheritParams cards::ard_stack
+#' @param variables ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
+#'   The categorical variables for which occurrence rates per unique ID (by maximum level) will be calculated.
+#' @param id ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
+#'   Argument used to subset `data` to identify rows in `data` to calculate categorical variable level occurrence rates.
+#' @param denominator (`data.frame`, `integer`)\cr
+#'   An optional argument to change the denominator used for `"N"` and `"p"` statistic calculations.
+#'   Defaults to `NULL`, in which case `dplyr::distinct(data, dplyr::pick(all_of(c(id, by))))` is used for these
+#'   calculations. See [cards::ard_categorical()] for more details on specifying denominators.
+#' @param quiet (scalar `logical`)\cr
+#'   Logical indicating whether to suppress additional messaging. Default is `FALSE`.
+#'
+#' @return an ARD data frame of class 'card'
+#' @name ard_categorical_max
+#'
+#' @examples
+#' # Occurrence Rates by Max Level (Highest Severity) --------------------------
+#' ard_categorical_max(
+#'   cards::ADAE,
+#'   variables = c(AESER, AESEV),
+#'   id = USUBJID,
+#'   by = TRTA,
+#'   denominator = cards::ADSL |> dplyr::rename(TRTA = ARM)
+#' )
+NULL
+
+#' @rdname ard_categorical_max
+#' @export
+ard_categorical_max <- function(data,
+                                variables,
+                                id,
+                                by = dplyr::group_vars(data),
+                                statistic = everything() ~ c("n", "p", "N"),
+                                denominator = NULL,
+                                fmt_fn = NULL,
+                                stat_label = everything() ~ cards::default_stat_labels(),
+                                quiet = FALSE,
+                                ...) {
+  set_cli_abort_call()
+
+  # check inputs ---------------------------------------------------------------
+  check_not_missing(data)
+  check_not_missing(variables)
+  check_not_missing(id)
+  cards::process_selectors(data, variables = {{ variables }}, id = {{ id }}, by = {{ by }})
+  data <- dplyr::ungroup(data)
+
+  # check the id argument is not empty
+  if (is_empty(id)) {
+    cli::cli_abort("Argument {.arg id} cannot be empty.", call = get_cli_abort_call())
+  }
+
+  # return empty ARD if no variables selected ----------------------------------
+  if (is_empty(variables)) {
+    return(dplyr::tibble() |> cards::as_card())
+  }
+
+  lst_results <- lapply(
+    variables,
+    function(x) {
+      ard_categorical(
+        data = data |>
+          arrange_using_order(c(id, by, x)) |>
+          dplyr::slice_tail(n = 1L, by = all_of(c(id, by))),
+        variables = all_of(x),
+        by = all_of(by),
+        statistic = statistic,
+        denominator = denominator,
+        fmt_fn = fmt_fn,
+        stat_label = stat_label
+      )
+    }
+  )
+
+  # print default order of variable levels -------------------------------------
+  for (v in variables) {
+    lvls <- .unique_and_sorted(data[[v]])
+    vec <- cli::cli_vec(
+      lvls,
+      style = list("vec-sep" = " < ", "vec-sep2" = " < ", "vec-last" = " < ", "vec-trunc" = 3)
+    )
+    if (!quiet) cli::cli_inform("{.var {v}}: {.val {vec}}")
+  }
+
+  # combine results ------------------------------------------------------------
+  result <- lst_results |>
+    dplyr::bind_rows() |>
+    dplyr::mutate(context = "categorical_max") |>
+    cards::tidy_ard_column_order() |>
+    cards::tidy_ard_row_order()
+
+  # return final result --------------------------------------------------------
+  result
+}
+
+# internal function copied from cards
+# like `dplyr::arrange()`, but uses base R's `order()` to keep consistency in some edge cases
+arrange_using_order <- function(data, columns) {
+  inject(data[with(data, order(!!!syms(columns))), ])
+}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index c129d233..f6e21349 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -88,6 +88,7 @@ reference:
       - ard_categorical_ci.data.frame
       - ard_regression
       - ard_regression_basic
+      - ard_categorical_max
 
   - title: "Helpers"
   - contents:
diff --git a/man/ard_categorical_max.Rd b/man/ard_categorical_max.Rd
new file mode 100644
index 00000000..25bb6a1a
--- /dev/null
+++ b/man/ard_categorical_max.Rd
@@ -0,0 +1,79 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ard_categorical_max.R
+\name{ard_categorical_max}
+\alias{ard_categorical_max}
+\title{ARD to Calculate Categorical Occurrence Rates by Maximum Level Per Unique ID}
+\usage{
+ard_categorical_max(
+  data,
+  variables,
+  id,
+  by = dplyr::group_vars(data),
+  statistic = everything() ~ c("n", "p", "N"),
+  denominator = NULL,
+  fmt_fn = NULL,
+  stat_label = everything() ~ cards::default_stat_labels(),
+  quiet = FALSE,
+  ...
+)
+}
+\arguments{
+\item{data}{(\code{data.frame})\cr
+a data frame}
+
+\item{variables}{(\code{\link[dplyr:dplyr_tidy_select]{tidy-select}})\cr
+The categorical variables for which occurrence rates per unique ID (by maximum level) will be calculated.}
+
+\item{id}{(\code{\link[dplyr:dplyr_tidy_select]{tidy-select}})\cr
+Argument used to subset \code{data} to identify rows in \code{data} to calculate categorical variable level occurrence rates.}
+
+\item{by}{(\code{\link[dplyr:dplyr_tidy_select]{tidy-select}})\cr
+columns to tabulate by in the series of ARD function calls.
+Any rows with \code{NA} or \code{NaN} values are removed from all calculations.}
+
+\item{statistic}{(\code{\link[cards:syntax]{formula-list-selector}})\cr
+a named list, a list of formulas,
+or a single formula where the list element one or more of  \code{c("n", "N", "p")}
+(or the RHS of a formula).}
+
+\item{denominator}{(\code{data.frame}, \code{integer})\cr
+An optional argument to change the denominator used for \code{"N"} and \code{"p"} statistic calculations.
+Defaults to \code{NULL}, in which case \code{dplyr::distinct(data, dplyr::pick(all_of(c(id, by))))} is used for these
+calculations. See \code{\link[cards:ard_categorical]{cards::ard_categorical()}} for more details on specifying denominators.}
+
+\item{fmt_fn}{(\code{\link[cards:syntax]{formula-list-selector}})\cr
+a named list, a list of formulas,
+or a single formula where the list element is a named list of functions
+(or the RHS of a formula),
+e.g. \verb{list(mpg = list(mean = \\(x) round(x, digits = 2) |> as.character()))}.}
+
+\item{stat_label}{(\code{\link[cards:syntax]{formula-list-selector}})\cr
+a named list, a list of formulas, or a single formula where
+the list element is either a named list or a list of formulas defining the
+statistic labels, e.g. \code{everything() ~ list(n = "n", p = "pct")} or
+\code{everything() ~ list(n ~ "n", p ~ "pct")}.}
+
+\item{quiet}{(scalar \code{logical})\cr
+Logical indicating whether to suppress additional messaging. Default is \code{FALSE}.}
+
+\item{...}{Arguments passed to methods.}
+}
+\value{
+an ARD data frame of class 'card'
+}
+\description{
+Function calculates categorical variable level occurrences rates by maximum level per unique ID.
+Each variable in \code{variables} is evaluated independently and then results for all variables are stacked.
+Only the highest-ordered level will be counted for each unique ID.
+Unordered, non-numeric variables will be converted to factor and the default level order used for ordering.
+}
+\examples{
+# Occurrence Rates by Max Level (Highest Severity) --------------------------
+ard_categorical_max(
+  cards::ADAE,
+  variables = c(AESER, AESEV),
+  id = USUBJID,
+  by = TRTA,
+  denominator = cards::ADSL |> dplyr::rename(TRTA = ARM)
+)
+}
diff --git a/tests/testthat/_snaps/ard_categorical_max.md b/tests/testthat/_snaps/ard_categorical_max.md
new file mode 100644
index 00000000..51937e20
--- /dev/null
+++ b/tests/testthat/_snaps/ard_categorical_max.md
@@ -0,0 +1,194 @@
+# ard_categorical_max() works with default settings
+
+    Code
+      print(res, n = 20, columns = "all")
+    Message
+      {cards} data frame: 27 x 11
+    Output
+         group1 group1_level variable variable_level   context stat_name stat_label  stat fmt_fn warning error
+      1    TRTA      Placebo    AESEV           MILD categori…         n          n    36      0              
+      2    TRTA      Placebo    AESEV           MILD categori…         N          N    69      0              
+      3    TRTA      Placebo    AESEV           MILD categori…         p          % 0.522   <fn>              
+      4    TRTA      Placebo    AESEV       MODERATE categori…         n          n    26      0              
+      5    TRTA      Placebo    AESEV       MODERATE categori…         N          N    69      0              
+      6    TRTA      Placebo    AESEV       MODERATE categori…         p          % 0.377   <fn>              
+      7    TRTA      Placebo    AESEV         SEVERE categori…         n          n     7      0              
+      8    TRTA      Placebo    AESEV         SEVERE categori…         N          N    69      0              
+      9    TRTA      Placebo    AESEV         SEVERE categori…         p          % 0.101   <fn>              
+      10   TRTA    Xanomeli…    AESEV           MILD categori…         n          n    22      0              
+      11   TRTA    Xanomeli…    AESEV           MILD categori…         N          N    79      0              
+      12   TRTA    Xanomeli…    AESEV           MILD categori…         p          % 0.278   <fn>              
+      13   TRTA    Xanomeli…    AESEV       MODERATE categori…         n          n    49      0              
+      14   TRTA    Xanomeli…    AESEV       MODERATE categori…         N          N    79      0              
+      15   TRTA    Xanomeli…    AESEV       MODERATE categori…         p          %  0.62   <fn>              
+      16   TRTA    Xanomeli…    AESEV         SEVERE categori…         n          n     8      0              
+      17   TRTA    Xanomeli…    AESEV         SEVERE categori…         N          N    79      0              
+      18   TRTA    Xanomeli…    AESEV         SEVERE categori…         p          % 0.101   <fn>              
+      19   TRTA    Xanomeli…    AESEV           MILD categori…         n          n    19      0              
+      20   TRTA    Xanomeli…    AESEV           MILD categori…         N          N    77      0              
+    Message
+      i 7 more rows
+      i Use `print(n = ...)` to see more rows
+
+---
+
+    Code
+      print(ard_categorical_max(dplyr::group_by(cards::ADAE, TRTA), variables = AESEV, id = USUBJID, denominator = dplyr::rename(cards::ADSL, TRTA = ARM)), n = 20, columns = "all")
+    Message
+      `AESEV`: "MILD" < "MODERATE" < "SEVERE"
+      {cards} data frame: 27 x 11
+    Output
+         group1 group1_level variable variable_level   context stat_name stat_label  stat fmt_fn warning error
+      1    TRTA      Placebo    AESEV           MILD categori…         n          n    36      0              
+      2    TRTA      Placebo    AESEV           MILD categori…         N          N    86      0              
+      3    TRTA      Placebo    AESEV           MILD categori…         p          % 0.419   <fn>              
+      4    TRTA      Placebo    AESEV       MODERATE categori…         n          n    26      0              
+      5    TRTA      Placebo    AESEV       MODERATE categori…         N          N    86      0              
+      6    TRTA      Placebo    AESEV       MODERATE categori…         p          % 0.302   <fn>              
+      7    TRTA      Placebo    AESEV         SEVERE categori…         n          n     7      0              
+      8    TRTA      Placebo    AESEV         SEVERE categori…         N          N    86      0              
+      9    TRTA      Placebo    AESEV         SEVERE categori…         p          % 0.081   <fn>              
+      10   TRTA    Xanomeli…    AESEV           MILD categori…         n          n    22      0              
+      11   TRTA    Xanomeli…    AESEV           MILD categori…         N          N    84      0              
+      12   TRTA    Xanomeli…    AESEV           MILD categori…         p          % 0.262   <fn>              
+      13   TRTA    Xanomeli…    AESEV       MODERATE categori…         n          n    49      0              
+      14   TRTA    Xanomeli…    AESEV       MODERATE categori…         N          N    84      0              
+      15   TRTA    Xanomeli…    AESEV       MODERATE categori…         p          % 0.583   <fn>              
+      16   TRTA    Xanomeli…    AESEV         SEVERE categori…         n          n     8      0              
+      17   TRTA    Xanomeli…    AESEV         SEVERE categori…         N          N    84      0              
+      18   TRTA    Xanomeli…    AESEV         SEVERE categori…         p          % 0.095   <fn>              
+      19   TRTA    Xanomeli…    AESEV           MILD categori…         n          n    19      0              
+      20   TRTA    Xanomeli…    AESEV           MILD categori…         N          N    84      0              
+    Message
+      i 7 more rows
+      i Use `print(n = ...)` to see more rows
+
+# ard_categorical_max(statistic) works
+
+    Code
+      ard_categorical_max(cards::ADAE, variables = AESEV, id = USUBJID, by = TRTA, denominator = dplyr::rename(cards::ADSL, TRTA = ARM), statistic = ~"n")
+    Message
+      `AESEV`: "MILD" < "MODERATE" < "SEVERE"
+      {cards} data frame: 9 x 11
+    Output
+        group1 group1_level variable variable_level stat_name stat_label stat
+      1   TRTA      Placebo    AESEV           MILD         n          n   36
+      2   TRTA      Placebo    AESEV       MODERATE         n          n   26
+      3   TRTA      Placebo    AESEV         SEVERE         n          n    7
+      4   TRTA    Xanomeli…    AESEV           MILD         n          n   22
+      5   TRTA    Xanomeli…    AESEV       MODERATE         n          n   49
+      6   TRTA    Xanomeli…    AESEV         SEVERE         n          n    8
+      7   TRTA    Xanomeli…    AESEV           MILD         n          n   19
+      8   TRTA    Xanomeli…    AESEV       MODERATE         n          n   42
+      9   TRTA    Xanomeli…    AESEV         SEVERE         n          n   16
+    Message
+      i 4 more variables: context, fmt_fn, warning, error
+
+# ard_categorical_max(denominator) works
+
+    Code
+      ard_categorical_max(cards::ADAE, variables = AESEV, id = USUBJID, by = TRTA)
+    Message
+      `AESEV`: "MILD" < "MODERATE" < "SEVERE"
+      {cards} data frame: 27 x 11
+    Output
+         group1 group1_level variable variable_level stat_name stat_label  stat
+      1    TRTA      Placebo    AESEV           MILD         n          n    36
+      2    TRTA      Placebo    AESEV           MILD         N          N    69
+      3    TRTA      Placebo    AESEV           MILD         p          % 0.522
+      4    TRTA      Placebo    AESEV       MODERATE         n          n    26
+      5    TRTA      Placebo    AESEV       MODERATE         N          N    69
+      6    TRTA      Placebo    AESEV       MODERATE         p          % 0.377
+      7    TRTA      Placebo    AESEV         SEVERE         n          n     7
+      8    TRTA      Placebo    AESEV         SEVERE         N          N    69
+      9    TRTA      Placebo    AESEV         SEVERE         p          % 0.101
+      10   TRTA    Xanomeli…    AESEV           MILD         n          n    22
+    Message
+      i 17 more rows
+      i Use `print(n = ...)` to see more rows
+      i 4 more variables: context, fmt_fn, warning, error
+
+---
+
+    Code
+      ard_categorical_max(cards::ADAE, variables = AESEV, id = USUBJID, by = TRTA, denominator = 100)
+    Message
+      `AESEV`: "MILD" < "MODERATE" < "SEVERE"
+      {cards} data frame: 27 x 11
+    Output
+         group1 group1_level variable variable_level stat_name stat_label stat
+      1    TRTA      Placebo    AESEV           MILD         n          n   36
+      2    TRTA      Placebo    AESEV           MILD         N          N  100
+      3    TRTA      Placebo    AESEV           MILD         p          % 0.36
+      4    TRTA      Placebo    AESEV       MODERATE         n          n   26
+      5    TRTA      Placebo    AESEV       MODERATE         N          N  100
+      6    TRTA      Placebo    AESEV       MODERATE         p          % 0.26
+      7    TRTA      Placebo    AESEV         SEVERE         n          n    7
+      8    TRTA      Placebo    AESEV         SEVERE         N          N  100
+      9    TRTA      Placebo    AESEV         SEVERE         p          % 0.07
+      10   TRTA    Xanomeli…    AESEV           MILD         n          n   22
+    Message
+      i 17 more rows
+      i Use `print(n = ...)` to see more rows
+      i 4 more variables: context, fmt_fn, warning, error
+
+# ard_categorical_max() works with pre-ordered factor variables
+
+    Code
+      print(res, n = 20, columns = "all")
+    Message
+      {cards} data frame: 27 x 11
+    Output
+         group1 group1_level variable variable_level   context stat_name stat_label  stat fmt_fn warning error
+      1    TRTA      Placebo    AESEV           MILD categori…         n          n    36      0              
+      2    TRTA      Placebo    AESEV           MILD categori…         N          N    86      0              
+      3    TRTA      Placebo    AESEV           MILD categori…         p          % 0.419   <fn>              
+      4    TRTA      Placebo    AESEV       MODERATE categori…         n          n    26      0              
+      5    TRTA      Placebo    AESEV       MODERATE categori…         N          N    86      0              
+      6    TRTA      Placebo    AESEV       MODERATE categori…         p          % 0.302   <fn>              
+      7    TRTA      Placebo    AESEV         SEVERE categori…         n          n     7      0              
+      8    TRTA      Placebo    AESEV         SEVERE categori…         N          N    86      0              
+      9    TRTA      Placebo    AESEV         SEVERE categori…         p          % 0.081   <fn>              
+      10   TRTA    Xanomeli…    AESEV           MILD categori…         n          n    22      0              
+      11   TRTA    Xanomeli…    AESEV           MILD categori…         N          N    84      0              
+      12   TRTA    Xanomeli…    AESEV           MILD categori…         p          % 0.262   <fn>              
+      13   TRTA    Xanomeli…    AESEV       MODERATE categori…         n          n    49      0              
+      14   TRTA    Xanomeli…    AESEV       MODERATE categori…         N          N    84      0              
+      15   TRTA    Xanomeli…    AESEV       MODERATE categori…         p          % 0.583   <fn>              
+      16   TRTA    Xanomeli…    AESEV         SEVERE categori…         n          n     8      0              
+      17   TRTA    Xanomeli…    AESEV         SEVERE categori…         N          N    84      0              
+      18   TRTA    Xanomeli…    AESEV         SEVERE categori…         p          % 0.095   <fn>              
+      19   TRTA    Xanomeli…    AESEV           MILD categori…         n          n    19      0              
+      20   TRTA    Xanomeli…    AESEV           MILD categori…         N          N    84      0              
+    Message
+      i 7 more rows
+      i Use `print(n = ...)` to see more rows
+
+# ard_categorical_max() errors with incomplete factor columns
+
+    Code
+      ard_categorical_max(dplyr::mutate(cards::ADAE, AESOC = factor(AESOC, levels = character(
+        0))), variables = AESOC, id = USUBJID, by = TRTA)
+    Condition
+      Error in `ard_categorical_max()`:
+      ! Factors with empty "levels" attribute are not allowed, which was identified in column "AESOC".
+
+---
+
+    Code
+      ard_categorical_max(dplyr::mutate(cards::ADAE, SEX = factor(SEX, levels = c("F",
+        "M", NA), exclude = NULL)), variables = SEX, id = USUBJID, by = TRTA)
+    Condition
+      Error in `ard_categorical_max()`:
+      ! Factors with NA levels are not allowed, which are present in column "SEX".
+
+# ard_categorical_max() works without any variables
+
+    Code
+      ard_categorical_max(data = cards::ADAE, variables = starts_with("xxxx"), id = USUBJID,
+      by = c(TRTA, AESEV))
+    Message
+      {cards} data frame: 0 x 0
+    Output
+      data frame with 0 columns and 0 rows
+
diff --git a/tests/testthat/test-ard_categorical_max.R b/tests/testthat/test-ard_categorical_max.R
new file mode 100644
index 00000000..075e3455
--- /dev/null
+++ b/tests/testthat/test-ard_categorical_max.R
@@ -0,0 +1,253 @@
+test_that("ard_categorical_max() works with default settings", {
+  withr::local_options(list(width = 200))
+
+  expect_message(
+    res <- ard_categorical_max(
+      cards::ADAE,
+      variables = AESEV,
+      id = USUBJID,
+      by = TRTA
+    )
+  )
+  expect_snapshot(res |> print(n = 20, columns = "all"))
+
+  expect_equal(
+    res |>
+      dplyr::filter(
+        group1_level == "Placebo",
+        variable_level == "SEVERE",
+        stat_name == "n"
+      ) |>
+      cards::get_ard_statistics(),
+    list(
+      n = cards::ADAE |>
+        dplyr::filter(
+          TRTA == "Placebo",
+          AESEV == "SEVERE"
+        ) |>
+        dplyr::slice_tail(n = 1L, by = all_of(c("USUBJID", "TRTA", "AESEV"))) |>
+        nrow()
+    )
+  )
+
+  # with denominator
+  expect_snapshot(
+    ard_categorical_max(
+      cards::ADAE |> dplyr::group_by(TRTA),
+      variables = AESEV,
+      id = USUBJID,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM)
+    ) |>
+      print(n = 20, columns = "all")
+  )
+
+  # with multiple variables
+  expect_message(expect_message(
+    res2 <- ard_categorical_max(
+      cards::ADAE,
+      variables = c(AESEV, AESER),
+      id = USUBJID,
+      by = TRTA
+    )
+  ))
+  expect_equal(unique(res2$variable), c("AESEV", "AESER"))
+  expect_equal(
+    res,
+    res2[-c(28:45), ]
+  )
+})
+
+test_that("ard_categorical_max(statistic) works", {
+  withr::local_options(list(width = 200))
+
+  expect_snapshot(
+    ard_categorical_max(
+      cards::ADAE,
+      variables = AESEV,
+      id = USUBJID,
+      by = TRTA,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM),
+      statistic = ~"n"
+    )
+  )
+})
+
+test_that("ard_categorical_max(denominator) works", {
+  withr::local_options(list(width = 200))
+
+  # default denominator
+  expect_snapshot(
+    ard_categorical_max(
+      cards::ADAE,
+      variables = AESEV,
+      id = USUBJID,
+      by = TRTA
+    )
+  )
+
+  # numeric denominator
+  expect_snapshot(
+    ard_categorical_max(
+      cards::ADAE,
+      variables = AESEV,
+      id = USUBJID,
+      by = TRTA,
+      denominator = 100
+    )
+  )
+})
+
+test_that("ard_categorical_max(quiet) works", {
+  withr::local_options(list(width = 200))
+
+  expect_silent(
+    ard_categorical_max(
+      cards::ADAE,
+      variables = c(AESER, AESEV),
+      id = USUBJID,
+      by = TRTA,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM),
+      quiet = TRUE
+    )
+  )
+})
+
+test_that("ard_categorical_max() works with pre-ordered factor variables", {
+  withr::local_options(list(width = 200))
+
+  # ordered factor variable
+  adae <- cards::ADAE |>
+    dplyr::mutate(AESEV = factor(cards::ADAE$AESEV, ordered = TRUE))
+  # unordered factor variable
+  adae_unord <- cards::ADAE |>
+    dplyr::mutate(AESEV = factor(cards::ADAE$AESEV, ordered = FALSE))
+
+  expect_message(
+    res <- ard_categorical_max(
+      adae_unord,
+      variables = AESEV,
+      id = USUBJID,
+      by = TRTA,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM),
+      ordered = TRUE
+    )
+  )
+  expect_snapshot(res |> print(n = 20, columns = "all"))
+
+  expect_equal(
+    res |>
+      dplyr::mutate(variable_level = as.character(unlist(variable_level))) |>
+      dplyr::filter(
+        group1_level == "Placebo",
+        variable_level == "MODERATE",
+        stat_name == "n"
+      ) |>
+      cards::get_ard_statistics(),
+    list(
+      n = adae |>
+        dplyr::arrange(AESEV) |>
+        dplyr::slice_tail(n = 1L, by = all_of(c("USUBJID", "TRTA"))) |>
+        dplyr::filter(
+          TRTA == "Placebo",
+          AESEV == "MODERATE"
+        ) |>
+        nrow()
+    )
+  )
+
+  expect_message(
+    res_unord <- ard_categorical_max(
+      adae_unord,
+      variables = AESEV,
+      id = USUBJID,
+      by = TRTA,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM)
+    )
+  )
+  expect_equal(res$stat[[1]], res_unord$stat[[1]])
+
+  expect_message(
+    res2 <- ard_categorical_max(
+      adae,
+      variables = AESEV,
+      id = USUBJID,
+      by = TRTA,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM)
+    )
+  )
+  expect_equal(res, res2, ignore_attr = "class")
+
+  # multiple variables
+  expect_message(expect_message(
+    res3 <- ard_categorical_max(
+      adae,
+      variables = c(SEX, AESEV),
+      id = USUBJID,
+      by = TRTA,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM),
+      ordered = c(FALSE, TRUE)
+    )
+  ))
+  expect_equal(res, res3[-c(1:18), ])
+
+  # named vector
+  expect_message(expect_message(
+    res4 <- ard_categorical_max(
+      adae,
+      variables = c(SEX, AESEV),
+      id = USUBJID,
+      by = TRTA,
+      denominator = cards::ADSL |> dplyr::rename(TRTA = ARM),
+      ordered = c(AESEV = TRUE, SEX = FALSE)
+    )
+  ))
+  expect_equal(res3, res4)
+})
+
+test_that("ard_categorical_max() errors with incomplete factor columns", {
+  # Check error when factors have no levels
+  expect_snapshot(
+    error = TRUE,
+    ard_categorical_max(
+      cards::ADAE |>
+        dplyr::mutate(AESOC = factor(AESOC, levels = character(0))),
+      variables = AESOC,
+      id = USUBJID,
+      by = TRTA
+    )
+  )
+
+  # Check error when factor has NA level
+  expect_snapshot(
+    error = TRUE,
+    ard_categorical_max(
+      cards::ADAE |>
+        dplyr::mutate(SEX = factor(SEX, levels = c("F", "M", NA), exclude = NULL)),
+      variables = SEX,
+      id = USUBJID,
+      by = TRTA
+    )
+  )
+})
+
+test_that("ard_categorical_max() works without any variables", {
+  expect_snapshot(
+    ard_categorical_max(
+      data = cards::ADAE,
+      variables = starts_with("xxxx"),
+      id = USUBJID,
+      by = c(TRTA, AESEV)
+    )
+  )
+})
+
+test_that("ard_categorical_max() follows ard structure", {
+  expect_message(
+    ard_categorical_max(
+      cards::ADAE,
+      variables = AESOC,
+      id = USUBJID
+    ) |>
+      cards::check_ard_structure(method = FALSE)
+  )
+})