## Abstract

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 language | English (US) |
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Pages (from-to) | 1-22 |

Number of pages | 22 |

Journal | Journal of Statistical Software |

Volume | 94 |

DOIs | |

State | Published - 2020 |

Externally published | Yes |

### Bibliographical note

Funding Information:This work was supported by AbbVie, Inc.

## Keywords

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