preprocess

Function to preprocess the data and obtain the design matrices corresponding
to a model with just interaction of the covariates with the treatment,
to a model with just the main effects and to a model just with the covariates
and one-hot encoding.

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 

preprocess function

<dl><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>subgr</dt>
<dd>(<code>character</code>) vector with the name of the subgroup variables
from which we want to obtain the subgroup treatment effect.</dd>
<dt>covars</dt>
<dd>(<code>character</code>) vector with the name of the variables that
we want to include in the model. The <code>subgr</code> variables have to be included
here.</dd>
<dt>data</dt>
<dd>(<code>data frame</code>) the data frame with the variables.</dd></dl>

Arguments

Data Preprocessing — preprocess

<dl>

<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>subgr</dt>
<dd>(<code>character</code>) vector with the name of the subgroup variables
from which we want to obtain the subgroup treatment effect.</dd>


<dt>covars</dt>
<dd>(<code>character</code>) vector with the name of the variables that
we want to include in the model. The <code>subgr</code> variables have to be included
here.</dd>


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

</dl>

preprocess: Data Preprocessing

Description

Usage

Value

Arguments

Examples