simul_pfs

Simulation of Progression Free Survival Times

Subgroup analyses are routinely performed in clinical trial
analyses. From a methodological perspective, two key issues of
subgroup analyses are multiplicity (even if only predefined subgroups
are investigated) and the low sample sizes of subgroups which lead to
highly variable estimates, see e.g. Yusuf et al (1991)
<doi:10.1001/jama.1991.03470010097038>. This package implements
subgroup estimates based on Bayesian shrinkage priors, see Carvalho et
al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In
addition, estimates based on penalized likelihood inference are
available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>.
The corresponding shrinkage based forest plots address the
aforementioned issues and can complement standard forest plots in
practical clinical trial analyses.

Isaac Gravestock

bonsaiforest

Shrinkage Based Forest Plots

Mar Vazquez Rabunal

Daniel Sabanés Bové

Marcel Wolbers

F. Hoffmann-La Roche AG 

simul_pfs function

<dl><dt>lp_aft</dt>
<dd>(<code>numeric</code>) linear predictor values for the accelerate failure time model (AFT).</dd>
<dt>sigma_aft</dt>
<dd>(<code>number</code>) standard deviation for the AFT model.</dd>
<dt>recr_duration</dt>
<dd>(<code>number</code>) duration of recruitment.</dd>
<dt>rate_cens</dt>
<dd>(<code>number</code>) rate for the exponentially distributed censoring process.</dd>
<dt>n_events</dt>
<dd>(<code>count</code>) number of events to reach for the study end.</dd>
<dt>add_uncensored_pfs</dt>
<dd>(<code>flag</code>) whether to add the uncensored PFS as well to the resulting
<code>data.frame</code>.</dd></dl>

Arguments

Simulation of Progression Free Survival Times — simul_pfs

<dl>

<dt>lp_aft</dt>
<dd>(<code>numeric</code>) linear predictor values for the accelerate failure time model (AFT).</dd>


<dt>sigma_aft</dt>
<dd>(<code>number</code>) standard deviation for the AFT model.</dd>


<dt>recr_duration</dt>
<dd>(<code>number</code>) duration of recruitment.</dd>


<dt>rate_cens</dt>
<dd>(<code>number</code>) rate for the exponentially distributed censoring process.</dd>


<dt>n_events</dt>
<dd>(<code>count</code>) number of events to reach for the study end.</dd>


<dt>add_uncensored_pfs</dt>
<dd>(<code>flag</code>) whether to add the uncensored PFS as well to the resulting
<code>data.frame</code>.</dd>

</dl>

simul_pfs: Simulation of Progression Free Survival Times

Description

Usage

Value

Arguments

Examples