R/data.R

In gu-mi/simASD: Enhancement of the Adaptive Signature Design (ASD) for Learning and Confirming in a Single Pivotal Trial

#' @title A simulated mega-dataset for illustrating the adaptive signature design (ASD) based on simulated datasets
#'
#' @description A dataset containing the survival times, censoring status, biomarkers and indicators for learn/confirm allocations.
#' This dataset is used as an example to illustrate how to perform a simulation study for evaluating key parameters in an adaptive
#' signature design (ASD).
#'
#' @usage data(data.simASD)
#'
#' @format A data frame with 70,000 rows and 13 variables:
#' \describe{
#'   \item{dataset}{an index for the 100 simulated datasets, each consisting of 700 subjects}
#'   \item{id}{unique subject IDs in each dataset}
#'   \item{trt}{treatment arm indicator (0 = control; 1 = treatment)}
#'   \item{y_duration}{survival time (in days)}
#'   \item{y_censor}{censoring status (0 = censored; 1 = event)}
#'   \item{x1-x3}{continuous biomarkers, generated from Uniform(0,1) distribution}
#'   \item{ME_30_flag-ME_70_flag}{indicator (0/1) for learn/confirm allocations: column name "30" means 30\% of subjects are
#'   in the learn stage, while 70\% are in the confirm stage}
#' }
#'
#' @source Results from an internal simulator at Eli Lilly and Company. The dataset is identical to the one used in the manuscript,
#' scenario: n=700, p=3, the strongest predictive biomarker effect.
#'
"data.simASD"