Credsubs: Multiplicity-adjusted subset identification

Patrick M. Schnell, Mark Fiecas, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1-22
Number of pages22
JournalJournal of Statistical Software
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020, American Statistical Association. All rights reserved.


  • Credible subgroups
  • Multiple hypothesis testing
  • R, Subset identification
  • Subgroup analysis


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