diff --git a/NAMESPACE b/NAMESPACE
index a9557619..f061225b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+export(brm_data)
 export(brm_formula)
 export(brm_marginal_data)
 export(brm_marginal_draws)
diff --git a/R/brm_data.R b/R/brm_data.R
new file mode 100644
index 00000000..443d0f0e
--- /dev/null
+++ b/R/brm_data.R
@@ -0,0 +1,185 @@
+#' @title Create an MMRM dataset.
+#' @export
+#' @family data
+#' @description Create a dataset to analyze with an MMRM.
+#' @return A classed tibble with attributes which denote features of
+#'   the data such as the treatment group and discrete time variables.
+#' @param data Data frame or tibble with longitudinal data.
+#' @param outcome Character of length 1, name of the outcome variable.
+#' @param role Character of length 1. Either `"response"` if `outcome`
+#'   is the raw response variable (e.g. AVAL) or `"change"` if `outcome`
+#'   is change from baseline (e.g. CHG).
+#' @param group Character of length 1, name of the treatment group variable.
+#' @param base Character of length 1, name of the baseline response variable.
+#'   Supply `NULL` to ignore or omit.
+#' @param time Character of length 1, name of the discrete time variable.
+#' @param patient Character of length 1, name of the patient ID variable.
+#' @param covariates Character vector of names of other covariates.
+#' @examples
+#' set.seed(0)
+#' sim <- brm_simulate()
+#' data <- tibble::as_tibble(sim$data)
+#' colnames(data) <- paste0("col_", colnames(data))
+#' data
+#' brm_data(
+#'   data = data,
+#'   outcome = "col_response",
+#'   role = "response",
+#'   group = "col_group",
+#'   time = "col_time",
+#'   patient = "col_patient"
+#' )
+brm_data <- function(
+  data,
+  outcome = "CHG",
+  role = "change",
+  base = NULL,
+  group = "TRT01P",
+  time = "AVISIT",
+  patient = "USUBJID",
+  covariates = character(0)
+) {
+  assert(is.data.frame(data), message = "data arg must be a data frame.")
+  out <- brm_data_new(
+    data = data,
+    outcome = as.character(outcome),
+    role = as.character(role),
+    base = base,
+    group = as.character(group),
+    time = as.character(time),
+    patient = as.character(patient),
+    covariates = as.character(covariates)
+  )
+  brm_data_validate(data = out)
+  out <- brm_data_select(data = out)
+  out <- brm_data_fill(out)
+  out
+}
+
+brm_data_new <- function(
+  data,
+  outcome,
+  role,
+  base,
+  group,
+  time,
+  patient,
+  covariates
+) {
+  out <- tibble::new_tibble(x = data, class = "brm_data")
+  structure(
+    out,
+    outcome = outcome,
+    role = role,
+    base = base,
+    group = group,
+    time = time,
+    patient = patient,
+    covariates = covariates
+  )
+}
+
+brm_data_validate <- function(data) {
+  outcome <- attr(data, "outcome")
+  role <- attr(data, "role")
+  base <- attr(data, "base")
+  group <- attr(data, "group")
+  time <- attr(data, "time")
+  patient <- attr(data, "patient")
+  covariates <- attr(data, "covariates")
+  assert(is.data.frame(data), message = "data must be a data frame")
+  assert(inherits(data, "brm_data"), message = "data not from brm_data()")
+  assert_chr(outcome, "outcome of data must be a nonempty character string")
+  assert_chr(role, "role of data must be a nonempty character string")
+  assert_chr(base %|||% "x", "base of data must NULL or character")
+  assert_chr(group, "group of data must be a nonempty character string")
+  assert_chr(time, "time of data must be a nonempty character string")
+  assert_chr(patient, "patient of data must be a nonempty character string")
+  assert_chr_vec(covariates, "covariates of data must be a character vector")
+  assert_col(outcome, data)
+  assert(
+    role %in% c("response", "change"),
+    message = "role must be either \"response\" or \"change\""
+  )
+  assert_col(base, data)
+  assert_col(group, data)
+  assert_col(time, data)
+  assert_col(patient, data)
+  assert_col(covariates, data)
+  for (column in c(base, group, time, patient, covariates)) {
+    assert(
+      !anyNA(data[[column]]),
+      message = sprintf(
+        "no missing values allowed in column \"%s\"",
+        column
+      )
+    )
+  }
+  for (column in c(group, time)) {
+    assert(
+      !is.numeric(data[[column]]),
+      message = sprintf(
+        paste(
+          "%s column in the data must not be numeric.",
+          "Should be character or factor."
+        ),
+        column
+      )
+    )
+  }
+}
+
+brm_data_select <- function(data) {
+  columns <- c(
+    attr(data, "outcome"),
+    attr(data, "base"),
+    attr(data, "group"),
+    attr(data, "time"),
+    attr(data, "patient"),
+    attr(data, "covariates")
+  )
+  columns <- as.character(columns)
+  data[, columns, drop = FALSE]
+}
+
+brm_data_fill <- function(data) {
+  outcome <- attr(data, "outcome")
+  role <- attr(data, "role")
+  base <- attr(data, "base")
+  group <- attr(data, "group")
+  time <- attr(data, "time")
+  patient <- attr(data, "patient")
+  covariates <- attr(data, "covariates")
+  args <- list(data = data, as.symbol(patient), as.symbol(time))
+  data <- do.call(what = tidyr::complete, args = args)
+  args <- list(.data = data, as.symbol(patient), as.symbol(time))
+  data <- do.call(what = dplyr::arrange, args = args)
+  for (column in c(base, group, covariates)) {
+    data[[column]] <- brm_data_fill_column(data[[column]], data[[patient]])
+  }
+  brm_data_new(
+    data = data,
+    outcome = outcome,
+    role = role,
+    base = base,
+    group = group,
+    time = time,
+    patient = patient,
+    covariates = covariates
+  )
+}
+
+brm_data_fill_column <- function(x, index) {
+  out <- tapply(
+    X = x,
+    INDEX = index,
+    FUN = brm_data_locf
+  )
+  unlist(out, use.names = FALSE)
+}
+
+brm_data_locf <- function(x) {
+  x <- zoo::na.locf(x, fromLast = FALSE, na.rm = FALSE)
+  x <- zoo::na.locf(x, fromLast = TRUE, na.rm = FALSE)
+  x
+}
diff --git a/R/utils_assert.R b/R/utils_assert.R
index 1bb5efa6..a633575b 100644
--- a/R/utils_assert.R
+++ b/R/utils_assert.R
@@ -21,6 +21,19 @@ assert <- function(
   }
 }
 
+assert_col <- function(value, data, message = NULL) {
+  message <- message %|||% paste(
+    paste(value, collapse = ", "),
+    "must be column name(s) of",
+    deparse(substitute(data))
+  )
+  assert(
+    all(value %in% colnames(data)),
+    message = message
+  )
+}
+
+
 assert_chr_vec <- function(value, message = NULL) {
   assert(
     value,
diff --git a/_pkgdown.yml b/_pkgdown.yml
index d9aa5de5..4943bf4e 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -3,8 +3,9 @@ reference:
 - title: Help
   contents:
   - '`brms.mmrm-package`'
-- title: Simulation
+- title: Data
   contents:
+  - '`brm_data`'
   - '`brm_simulate`'
 - title: Models
   contents:
diff --git a/man/brm_data.Rd b/man/brm_data.Rd
new file mode 100644
index 00000000..f69491fa
--- /dev/null
+++ b/man/brm_data.Rd
@@ -0,0 +1,60 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/brm_data.R
+\name{brm_data}
+\alias{brm_data}
+\title{Create an MMRM dataset.}
+\usage{
+brm_data(
+  data,
+  outcome = "CHG",
+  role = "change",
+  base = NULL,
+  group = "TRT01P",
+  time = "AVISIT",
+  patient = "USUBJID",
+  covariates = character(0)
+)
+}
+\arguments{
+\item{data}{Data frame or tibble with longitudinal data.}
+
+\item{outcome}{Character of length 1, name of the outcome variable.}
+
+\item{role}{Character of length 1. Either \code{"response"} if \code{outcome}
+is the raw response variable (e.g. AVAL) or \code{"change"} if \code{outcome}
+is change from baseline (e.g. CHG).}
+
+\item{base}{Character of length 1, name of the baseline response variable.
+Supply \code{NULL} to ignore or omit.}
+
+\item{group}{Character of length 1, name of the treatment group variable.}
+
+\item{time}{Character of length 1, name of the discrete time variable.}
+
+\item{patient}{Character of length 1, name of the patient ID variable.}
+
+\item{covariates}{Character vector of names of other covariates.}
+}
+\value{
+A classed tibble with attributes which denote features of
+the data such as the treatment group and discrete time variables.
+}
+\description{
+Create a dataset to analyze with an MMRM.
+}
+\examples{
+set.seed(0)
+sim <- brm_simulate()
+data <- tibble::as_tibble(sim$data)
+colnames(data) <- paste0("col_", colnames(data))
+data
+brm_data(
+  data = data,
+  outcome = "col_response",
+  role = "response",
+  group = "col_group",
+  time = "col_time",
+  patient = "col_patient"
+)
+}
+\concept{data}