credsubs: Multiplicity-Adjusted Subset Identification

credsubs: Multiplicity-Adjusted Subset Identification

: credible subgroups, multiple hypothesis testing, R, subset identification, subgroup
analysis

Subset identification methods are used to select the subset of a covariate space over
which the conditional distribution of a response has certain properties – for example,
identifying types of patients whose conditional treatment effect is positive. An often
critical requirement of subset identification methods is multiplicity control, by which the
family-wise Type I error rate is controlled, rather than the Type I error rate of each
covariate-determined hypothesis separately. The credible subset (or credible subgroup)
method provides a multiplicity-controlled estimate of the target subset in the form of two
bounding subsets: one which entirely contains the target subset, and one which is entirely
contained by it.
We introduce a new R package, credsubs, which constructs credible subset estimates
using a sample from the joint posterior distribution of any regression model, a description
of the covariate space, and a function mapping the parameters and covariates to the subset
criterion. We demonstrate parametric and nonparametric applications of the package to
a clinical trial dataset and a neuroimaging dataset, respectively.

Patrick M. Schnell

https://www.jstatsoft.org/article/view/v094i07

The Ohio State University

June 2020

Fajar Bagus W

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