<p>Survival times of 686 patients with primary node positive breast cancer.</p>

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

Paul Metcalfe

Jordan Amdahl

bc function

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

bc: Breast cancer survival data

Description

Usage

Arguments

Format

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).