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| Authors: | Patrick M. Schnell, Mark Fiecas, Bradley P. Carlin | ||||||
| Title: | credsubs: Multiplicity-Adjusted Subset Identification | ||||||
| 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. | ||||||
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Page views:: 815. Submitted: 2017-08-23. Published: 2020-09-02. |
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| Paper: |
credsubs: Multiplicity-Adjusted Subset Identification
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| DOI: |
10.18637/jss.v094.i07
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This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |