TY - JOUR
AU - Schnell, Patrick M.
AU - Fiecas, Mark
AU - Carlin, Bradley P.
PY - 2020/09/02
Y2 - 2024/10/07
TI - credsubs: Multiplicity-Adjusted Subset Identification
JF - Journal of Statistical Software
JA - J. Stat. Soft.
VL - 94
IS - 7
SE - Articles
DO - 10.18637/jss.v094.i07
UR - https://www.jstatsoft.org/index.php/jss/article/view/v094i07
SP - 1 - 22
AB - 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.
ER -