dataCSL

<p>Survival status for the liver chirrosis patients of Schlichting et al.</p>

dataset

We propose numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events (Le Borgne, 2016, <doi:10.1002/sim.6777>), competing events (Trebern-Launay, 2018, <doi: 10.1007/s10654-017-0322-3>), and multi-state data (Gillaizeau, 2018, <doi: 10.1002/sim.7550>). For multistate data, semi-Markov model with interval censoring (Foucher, 2008, <doi: 10.1177/0962280208093889>) may be considered and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables (Gillaizeau, 2017, <doi: 10.1177/0962280215586456>). For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders (Le Borgne, 2018,  <doi: 10.1177/0962280217702416>). Finally, several functions are available to assess time-dependent ROC curves (Combescure, 2017, <doi: 10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi: 10.1002/sim.6111>) from aggregated data.

Y. Foucher

RISCA

Causal Inference and Prediction in Cohort-Based Analyses

Yohann Foucher

Florent Le Borgne

Etienne Dantan

Florence Gillaizeau

Arthur Chatton

Christophe Combescure

dataCSL function

<p>This data frame contains the following columns:</p><table><tr><td>
<code>id</code> </td>
<td> a numeric vector related to the subject ID. </td>
</tr><tr><td>									
<code>time</code> </td>
<td> A numeric vector with the time of measurement. </td>
</tr><tr><td>
<code>prot</code> </td>
<td> A numeric vector with the prothrombin level at measurement time. </td>
</tr><tr><td>
<code>dc</code> </td>
<td> A numeric vector code. 0: censored observation, 1: died at eventT. </td>
</tr><tr><td>
<code>eventT</code> </td>
<td> A numeric vector with the time of event (death). </td>
</tr><tr><td>
<code>treat</code> </td>
<td> A numeric vector code. 0: active treatment of prednisone, 1: placebo treatment.  </td>
</tr><tr><td>
<code>sex</code> </td>
<td> A numeric vector code. 0: female, 1: male.  </td>
</tr><tr><td>
<code>age</code> </td>
<td> A numeric vector with the age of subject at inclusion time subtracted 60.  </td>
</tr><tr><td>
<code>prot.base</code> </td>
<td>  A numeric vector with the prothrombin base level before entering the study </td>
</tr><tr><td>
<code>prot.prev</code> </td>
<td> A numeric vector with the level of prothrombin at previous measurement time.  </td>
</tr><tr><td>
<code>lt</code> </td>
<td>  A numeric vector with the starting time for the time-interval. </td>
</tr></table>

Format

CSL Liver Chirrosis Data. — dataCSL

CSL Liver Chirrosis Data.

dataCSL: CSL Liver Chirrosis Data.

Description

Usage

Arguments

Format

References

Examples

`id`	a numeric vector related to the subject ID.
`time`	A numeric vector with the time of measurement.
`prot`	A numeric vector with the prothrombin level at measurement time.
`dc`	A numeric vector code. 0: censored observation, 1: died at eventT.
`eventT`	A numeric vector with the time of event (death).
`treat`	A numeric vector code. 0: active treatment of prednisone, 1: placebo treatment.
`sex`	A numeric vector code. 0: female, 1: male.
`age`	A numeric vector with the age of subject at inclusion time subtracted 60.
`prot.base`	A numeric vector with the prothrombin base level before entering the study
`prot.prev`	A numeric vector with the level of prothrombin at previous measurement time.
`lt`	A numeric vector with the starting time for the time-interval.