Survival times of 686 patients with primary node positive breast
  cancer.

datasets

Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions.   Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard.  There are also tools for fitting and predicting from fully parametric multi-state models.

Christopher Jackson

flexsurv

Flexible Parametric Survival and Multi-State Models

bc function

A data frame with 686 rows. 
    <tabular>rll</tabular>{
    <code>censrec</code> 	(numeric) 	1=dead, 0=censored <code>rectime</code> 	(numeric) 	Time of death or censoring in days<code>group</code> 	(numeric) 	Prognostic group:
    <code>"Good"</code>,<code>"Medium"</code> or <code>"Poor"</code>, 		from a regression model developed by
              Sauerbrei and Royston (1999).}

format

German Breast Cancer Study Group, 1984-1989.  Used as a reference
  dataset for the spline-based survival model of Royston and Parmar
  (2002), implemented here in <code><a href="/link/flexsurvspline?package=flexsurv&version=0.6" rd-options="" data-mini-rdoc="flexsurv::flexsurvspline">flexsurvspline</a></code>.  Originally
  provided with the <code>stpm</code> (Royston 2001, 2004) and <code>stpm2</code>
  (Lambert 2009, 2010) Stata modules.

bc: Breast cancer survival data

Description

Usage

Arguments

format

source

References

See Also