title: A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects.
abstract: Many new experimental treatments benefit only a subset of the population. Identifying the baseline covariate profiles of patients who benefit from such a treatment, rather than determining whether or not the treatment has a population-level effect, can substantially lessen the risk in undertaking a clinical trial and expose fewer patients to treatments that do not benefit them. The standard analyses for identifying patient subgroups that benefit from an experimental treatment either do not account for multiplicity, or focus on testing for the presence of treatment-covariate interactions rather than the resulting individualized treatment effects. We propose a Bayesian credible subgroups method to identify two bounding subgroups for the benefiting subgroup: one for which it is likely that all members simultaneously have a treatment effect exceeding a specified threshold, and another for which it is likely that no members do. We examine frequentist properties of the credible subgroups method via simulations and illustrate the approach using data from an Alzheimer's disease treatment trial. We conclude with a discussion of the advantages and limitations of this approach to identifying patients for whom the treatment is beneficial.
authors.affiliation: Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, U.S.A.
authors.forename: Patrick M
authors.initials: PM
authors.lastname: Schnell
authors.affiliation: AbbVie, North Chicago, Illinois, U.S.A.
authors.forename: Qi
authors.initials: Q
authors.lastname: Tang
authors.affiliation: AbbVie, North Chicago, Illinois, U.S.A.
authors.forename: Walter W
authors.initials: WW
authors.lastname: Offen
authors.affiliation: Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, U.S.A.
authors.forename: Bradley P
authors.initials: BP
authors.lastname: Carlin
mesh_terms: D000544:Alzheimer Disease / Q000188:drug therapy; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D018579:Patient Selection; D012074:Remission Induction; D016896:Treatment Outcome
keywords: Bayesian inference; Clinical trials; Heterogeneous treatment effect; Linear model; Simultaneous inference; Subgroup identification