simul_covariates

This function uses a block diagonal covariance matrix for the underlying
multivariate normal data to create the design matrix in blocks of 10, see
the details.

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_covariates function

<dl><dt>n</dt>
<dd>(<code>count</code>) number of rows (observations).</dd>
<dt>p_catvar</dt>
<dd>(<code>count</code>) number of covariates (excluding treatment arm).</dd>
<dt>add_contvars</dt>
<dd>(<code>flag</code>) whether to add continuous covariates.</dd>
<dt>arm_factor</dt>
<dd>(<code>flag</code>) whether to make the arm variable a factor.</dd></dl>

Arguments

Generation of a Design Matrix for Simulations — simul_covariates

<dl>

<dt>n</dt>
<dd>(<code>count</code>) number of rows (observations).</dd>


<dt>p_catvar</dt>
<dd>(<code>count</code>) number of covariates (excluding treatment arm).</dd>


<dt>add_contvars</dt>
<dd>(<code>flag</code>) whether to add continuous covariates.</dd>


<dt>arm_factor</dt>
<dd>(<code>flag</code>) whether to make the arm variable a factor.</dd>

</dl>

simul_covariates: Generation of a Design Matrix for Simulations

Description

Usage

Value

Arguments

Details

Examples