naivepop

Function to fit the naive overall population model to the data. This model is going to
estimate the subgroup treatment effects with the overall population
treatment effect.

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 

naivepop function

<dl><dt>resp</dt>
<dd>(<code>string</code>) the response variable name.</dd>
<dt>trt</dt>
<dd>(<code>string</code>) the treatment variable name. The treatment variable
must be a factor with 2 levels where the first level is the control and the
second one the treatment.</dd>
<dt>data</dt>
<dd>(<code>data frame</code>) the data frame with the variables.</dd>
<dt>resptype</dt>
<dd>(<code>string</code>) the type of data used. Can be "survival"
or "binary".</dd>
<dt>status</dt>
<dd>(<code>string</code>) only for "survival" <code>resptype</code>,
the status variable name in survival data.</dd></dl>

Arguments

Naive Overall Population Model Estimation — naivepop

<dl>

<dt>resp</dt>
<dd>(<code>string</code>) the response variable name.</dd>


<dt>trt</dt>
<dd>(<code>string</code>) the treatment variable name. The treatment variable
must be a factor with 2 levels where the first level is the control and the
second one the treatment.</dd>


<dt>data</dt>
<dd>(<code>data frame</code>) the data frame with the variables.</dd>


<dt>resptype</dt>
<dd>(<code>string</code>) the type of data used. Can be "survival"
or "binary".</dd>


<dt>status</dt>
<dd>(<code>string</code>) only for "survival" <code>resptype</code>,
the status variable name in survival data.</dd>

</dl>

naivepop: Naive Overall Population Model Estimation

Description

Usage

Value

Arguments

Examples