
  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. 
    <table><tr><td>
    <code>censrec</code> </td>
<td> (numeric) </td>
</tr><tr><td> 1=dead, 0=censored </td>
<td>
    <code>rectime</code> </td>
</tr><tr><td> (numeric) </td>
<td> Time of death or censoring in days</td>
</tr><tr><td>
    <code>group</code> </td>
<td> (numeric) </td>
</tr><tr><td> Prognostic group:
    <code>"Good"</code>,<code>"Medium"</code> or <code>"Poor"</code>, </td>
<td>
    </td>
</tr><tr><td> </td>
<td> from a regression model developed by
              Sauerbrei and Royston (1999).</td>
</tr></table>

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 rd-options="" href="/link/flexsurvspline?package=flexsurv&version=1.0.0" 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

`censrec`	(numeric)
1=dead, 0=censored	`rectime`
(numeric)	Time of death or censoring in days
`group`	(numeric)
Prognostic group: `"Good"`,`"Medium"` or `"Poor"`,
	from a regression model developed by Sauerbrei and Royston (1999).