R/data.R

In LTRCforests: Ensemble Methods for Survival Data with Time-Varying Covariates

#' Sample Mayo Clinic Primary Biliary Cirrhosis Data
#'
#' A sample real dataset with time varying covariates. It contains multiple records
#' of measurements of risk factors at multiple time points from 10 patients
#' with primary biliary cirrhosis (PBC), constructed from
#' \code{\link{pbcseq}} in the R package \pkg{\link{survival}}. The data
#' structure has been reformatted with left-truncated right-censored
#' pseudo-subject observations based on the Andersen-Gill reformulation method.
#'
#' @returns A data frame with 57 rows and 9 variables:
#'   \item{ID}{patient id.}
#'   \item{Start}{the left truncation time point for the corresponding
#'   pseudo-subject observation.}
#'   \item{Stop}{the right censoring time point for the corresponding
#'   pseudo-subject observation.}
#'   \item{Event}{a binary value, with \code{1} indicating the event occurring at
#'   the corresponding \code{Stop}, \code{0} indicating right-censored.}
#'   \item{age}{patient's age at entry, in years.}
#'   \item{alk.phos}{alkaline phosphotase (U/liter).}
#'   \item{ast}{aspartate aminotransferase (U/ml).}
#'   \item{chol}{serum cholesterol (mg/dl).}
#'   \item{edema}{\code{0}--no edema, \code{0.5}--untreated or successfully treated,
#'   \code{1}--edema despite diuretic therapy.}
#' @source P. A. Murtaugh, E. R. Dickson, G. M. V. Dam,
#' M. Malinchoc, P. M. Grambsch, A. L. Langworthy, and C. H. Gips. (1989).
#' Primary biliary cirrhosis: Prediction of shortterm survival based on
#' repeated patient visits. \emph{Hepatology}, \strong{20}, 126-134.
#' @references Andersen, P. and Gill, R. (1982). Cox's regression model for counting
#' processes, a large sample study. \emph{Annals of Statistics}, \strong{10}, 1100-1120.
#' @docType data
#' @name pbcsample
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