design_matrix1

<p>The design matrix from <code>example_data</code> considering just observations
from subgroup x_1a.</p>

datasets

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 

design_matrix1 function

<p>A <code>matrix</code> with 0 and 1 indicating the subgroups of each of the observations
and their interactions with the treatment variable.</p>

Format

Design Matrix Subgroup x_1a — design_matrix1

Design Matrix Subgroup x_1a

design_matrix1: Design Matrix Subgroup x_1a

Description

Usage

Arguments

Format