pred_s.t.bootstrap.tps

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).

Léonie Courcoul

FlexVarJM

Estimate Joint Models with Subject-Specific Variance

Antoine Barbieri

Hélène Jacqmin-Gadda

pred_s.t.bootstrap.tps function

<dl><dt>newdata</dt>
<dd>data frame : collected data for a new individual</dd>
<dt>object</dt>
<dd>lsjm object : estimation of the model</dd>
<dt>s</dt>
<dd>numeric : the time to begin prediction</dd>
<dt>window</dt>
<dd>numeric : the side of the prediction window</dd>
<dt>event</dt>
<dd>integer (0, 1 or 2) : the event of interest for the prediction</dd>
<dt>nb.draws</dt>
<dd>integer : the number of draws to compute the IC</dd></dl>

Arguments

Predictions computation — pred_s.t.bootstrap.tps

<dl>

<dt>newdata</dt>
<dd>data frame : collected data for a new individual</dd>


<dt>object</dt>
<dd>lsjm object : estimation of the model</dd>


<dt>s</dt>
<dd>numeric : the time to begin prediction</dd>


<dt>window</dt>
<dd>numeric : the side of the prediction window</dd>


<dt>event</dt>
<dd>integer (0, 1 or 2) : the event of interest for the prediction</dd>


<dt>nb.draws</dt>
<dd>integer : the number of draws to compute the IC</dd>

</dl>

pred_s.t.bootstrap.tps: Predictions computation

Description

Usage

Arguments