R/example_data.R

In PDRobust: Robust Longitudinal Effects Under Truncation by Death

#' Binary longitudinal example data
#'
#' Simulated long-format data for illustrating binary-outcome analyses.
#' Potential survival is generated with \eqn{S^1 \ge S^0}, matching the
#' package's treatment-1 survival-favorable convention. The simulation variables
#' are not observed counterfactual information available in a real study.
#'
#' @format A simulated long-format data frame with 1,200 rows (400 subjects
#'   at three visits) and 16 variables:
#' \describe{
#'   \item{id}{Subject identifier.}
#'   \item{time}{Analysis time.}
#'   \item{Pi}{Simulated probability of treatment 1 conditional on baseline
#'     covariates, stored to three decimal places.}
#'   \item{S1, S0}{Simulated potential survival indicators under treatment
#'     1 and 0, respectively; 1 denotes alive and 0 denotes dead.}
#'   \item{Y1, Y0}{Simulated binary potential outcomes under treatment 1 and
#'     0, respectively. These simulation variables are retained for illustration;
#'     package analyses use the observed outcome \code{Y}.}
#'   \item{X1, X2, X3}{Continuous baseline covariates.}
#'   \item{X4, X5, X6}{Binary baseline covariates.}
#'   \item{A}{Binary treatment indicator.}
#'   \item{S}{Binary survival or intermediate-status indicator.}
#'   \item{Y}{Binary outcome, structurally missing after death.}
#' }
#'
#' @source Simulated for package examples.
#'
#' @examples
#' data("BiSample", package = "PDRobust")
#' head(BiSample)
"BiSample"


#' Imperfect Continuous Longitudinal Example Data
#'
#' A deliberately imperfect continuous-outcome longitudinal data set derived
#' from a simulated continuous-outcome panel. The data mimic common issues
#' encountered in raw clinical data exports while remaining recoverable using
#' \code{\link{DataCheck}} and \code{\link{DataStandard}} with
#' \code{drop = TRUE}.
#'
#' The data include nonstandard subject identifiers, character-encoded visit
#' times and binary variables, unsorted records, an incomplete longitudinal
#' record, missing required covariate values, a missing outcome among survivors,
#' and a record with a missing subject identifier. Structural outcome
#' missingness for records with \code{alive_status = 0} is retained.
#'
#' @format A data frame with 599 rows and 11 variables in long format,
#' with one row per recorded subject and visit:
#' \describe{
#' \item{\code{patient_id}}{Noncanonical character subject identifier.}
#' \item{\code{visit_month}}{Character-encoded visit time in months.}
#' \item{\code{treatment}}{Character-encoded binary treatment assignment.}
#' \item{\code{alive_status}}{Character-encoded binary survival or intermediate status.}
#' \item{X1, X2, X3}{Continuous baseline covariates.}
#' \item{X4, X5, X6}{Binary baseline covariates.}
#' \item{\code{clinical_outcome}}{Continuous longitudinal clinical outcome.}
#' }
#'
#' @source Simulated for package examples.
#' @examples
#' data("ImperfectConSample", package = "PDRobust")
#' head(ImperfectConSample)
"ImperfectConSample"