<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,
based on either cause-specific hazards or mixture models.

Christopher Jackson

flexsurv

Flexible Parametric Survival and Multi-State Models

Paul Metcalfe

Jordan Amdahl

Matthew T. Warkentin

Michael Sweeting

Kevin Kunzmann

bc function

<p>A data frame with 686 rows.</p><table class="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><p></p><p></p>

Format

Breast cancer survival data — bc

<p>A data frame with 686 rows.</p><table class='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).