summary.elastic_net

Function to obtain the naive subgroup treatment effects of an object fitted
with the <code>elastic_net</code> function.

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 

summary.elastic_net function

<dl><dt>object</dt>
<dd>(<code>elastic_net</code>) the elastic_net object.</dd>
<dt>gamma</dt>
<dd>(<code>scalar</code>) numeric value defining the weights to obtain
the average hazard ratio. Default is 1 (in this case the average hazard
ratio obtained can be interpreted as the odds of concordance). Just needed
when using survival data.</dd>
<dt>l</dt>
<dd>(<code>scalar</code>) the maximum value of time that wants to be studied to
obtain the average hazard ratio. Default is the maximum value of time when
there was an event. Just needed when using survival data.</dd>
<dt>lambda</dt>
<dd>(<code>scalar</code>) the penalization constant in the elastic net.
Default is the value that leads to minimal cross validation error.</dd>
<dt>...</dt>
<dd>Arguments of summary</dd></dl>

Arguments

Summary Elastic Net Function — summary.elastic_net

<dl>

<dt>object</dt>
<dd>(<code>elastic_net</code>) the elastic_net object.</dd>


<dt>gamma</dt>
<dd>(<code>scalar</code>) numeric value defining the weights to obtain
the average hazard ratio. Default is 1 (in this case the average hazard
ratio obtained can be interpreted as the odds of concordance). Just needed
when using survival data.</dd>


<dt>l</dt>
<dd>(<code>scalar</code>) the maximum value of time that wants to be studied to
obtain the average hazard ratio. Default is the maximum value of time when
there was an event. Just needed when using survival data.</dd>


<dt>lambda</dt>
<dd>(<code>scalar</code>) the penalization constant in the elastic net.
Default is the value that leads to minimal cross validation error.</dd>


<dt>...</dt>
<dd>Arguments of summary</dd>

</dl>

summary.elastic_net: Summary Elastic Net Function

Description

Usage

Value

Arguments

Examples