R/data.R

In truncAIPW: Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation

#' A Simulated Data Set under Left Truncation but No Right Censoring
#'
#'
#' A simulated data set under left truncation but no right censoring.
#' The left truncation time and the event time are dependent via the two covariates `Z1' and `Z2'.
#' Under the data generating mechanism,
#' the conditional distribution of the event time given covariates follows a Cox proportional hazards model in the full data, and
#' the conditional distribution of the left truncation time given covariates follows a Cox proportional hazards model on the reversed time scale in the full data.
#' The truncation rate is 29.5\%;
#' and the truth \eqn{P^*(T>3) = 0.5755753}.
#'
#' @docType data
#'
#' @format A data frame with 500 subjects (rows) and 5 variables (columns).
#' \describe{
#'   \item{time}{the event time}
#'   \item{Q}{the left truncation time}
#'   \item{delta}{the event indicator}
#'   \item{Z1}{a continous covariate}
#'   \item{Z2}{a binary covariate}
#' }
#'
#' @source Simulated
#'
#' @usage data(simu)
#'
#' @keywords datasets
#'
#' @examples
#' data(simu)
"simu"





#' A Simulated Data Set under Left Truncation and Right Censoring where Censoring can be before Truncation
#'
#'
#' A simulated data set under left truncation and right censoring where censoring can be before left truncation.
#' The left truncation time and the event time are dependent via the two covariates `Z1' and `Z2'.
#' Under the data generating mechanism,
#' the conditional distribution of the censored event time X given covariates follows a Cox proportional hazards model in the full data,
#' and the conditional distribution of the left truncation time given covariates follows a Cox proportional hazards model on the reversed time scale in the full data.
#' The truncation rate is 29.5\%; the censoring rate is 16.5\%; \eqn{P^*(C<Q) = 0.6057};
#' and the truth \eqn{P^*(T>3) = 0.623955}.
#'
#' @docType data
#'
#' @format A data frame with 500 rows and 5 variables.
#' \describe{
#'   \item{X}{the censored event time X = min(T,C)}
#'   \item{Q}{the left truncation time}
#'   \item{delta}{the event indicator}
#'   \item{Z1}{a continous covariate}
#'   \item{Z2}{a binary covariate}
#' }
#'
#' @source Simulated
#'
#' @usage data(simu_c1)
#'
#' @keywords datasets
#'
#' @examples
#' data(simu_c1)
"simu_c1"










#' A Simulated Data Set under Left Truncation and Right Censoring where Censoring is always after Truncation
#'
#'
#' A simulated data set under left truncation and right censoring where censoring is always after left truncation.
#' The left truncation time and the event time are dependent via two covariates `Z1' and `Z2'.
#' Under the data generating mechanism,
#' the conditional distributions of the event time given covariates follows a Cox model in the full data,
#' and the conditional distribution of the left truncation time given covariates follows a Cox model on the reversed time scale  in the full data.
#' The truncation rate is 29.5\%; the censoring rate is 27.1\%;
#' and the truth \eqn{P^*(T>3) = 0.576547}.
#'
#' @docType data
#'
#' @format A data frame with 500 rows and 5 variables.
#' \describe{
#'   \item{X}{the censored event time X = min(T,C)}
#'   \item{Q}{the left truncation time}
#'   \item{delta}{the event indicator}
#'   \item{Z1}{a continous covariate}
#'   \item{Z2}{a binary covariate}
#' }
#'
#' @source Simulated
#'
#' @usage data(simu_c2)
#'
#' @keywords datasets
#'
#' @examples
#' data(simu_c2)
"simu_c2"