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#' German Breast Cancer Study Group
#'
#' A data frame containing the observations from the GBSG study.
#'
#' @format This data frame contains the observations of 686 women:
#' \describe{
#' \item{id}{patient id.}
#' \item{htreat}{hormonal therapy, a factor at two levels \code{0} (no) and \code{1} (yes).}
#' \item{age}{of the patients in years.}
#' \item{menostat}{menopausal status, a factor at two levels \code{1} (premenopausal) and \code{2} (postmenopausal).}
#' \item{tumsize}{tumor size (in mm).}
#' \item{tumgrad}{tumor grade, a ordered factor at levels \code{1 < 2 < 3}.}
#' \item{posnodal}{number of positive nodes.}
#' \item{prm}{progesterone receptor (in fmol).}
#' \item{esm}{estrogen receptor (in fmol).}
#' \item{rfst}{recurrence free survival time (in days).}
#' \item{cens}{censoring indicator (\code{0} censored, \code{1} event).}
#' }
#'
#' @references M. Schumacher, G. Basert, H. Bojar, K. Huebner, M. Olschewski,
#' W. Sauerbrei, C. Schmoor, C. Beyerle, R.L.A. Neumann and H.F. Rauschecker
#' for the German Breast Cancer Study Group (1994).
#' Randomized \eqn{2 \times 2} trial evaluating hormonal treatment
#' and the duration of chemotherapy in node-positive breast cancer patients.
#' \emph{Journal of Clinical Oncology}, \bold{12}, 2086--2093.\cr
#' W. Sauerbrei and P. Royston (1999). Building multivariable prognostic
#' and diagnostic models: transformation of the predictors by using
#' fractional polynomials. \emph{Journal of the Royal Statistics Society
#' Series A}, Volume \bold{162}(1), 71--94.
#'
#' @keywords datasets
#'
#' @examples
#' data("GBSG")
#' summary(GBSG)
"GBSG"
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