title: A Bayesian approach to subgroup identification.
abstract: This article discusses subgroup identification, the goal of which is to determine the heterogeneity of treatment effects across subpopulations. Searching for differences among subgroups is challenging because it is inherently a multiple testing problem with the complication that test statistics for subgroups are typically highly dependent, making simple multiplicity corrections such as the Bonferroni correction too conservative. In this article, a Bayesian approach to identify subgroup effects is proposed, with a scheme for assigning prior probabilities to possible subgroup effects that accounts for multiplicity and yet allows for (preexperimental) preference to specific subgroups. The analysis utilizes a new Bayesian model selection methodology and, as a by-product, produces individual probabilities of treatment effect that could be of use in personalized medicine. The analysis is illustrated on an example involving subgroup analysis of biomarker effects on treatments.
authors.affiliation: a Department of Statistical Science , Duke University , Durham , North Carolina , USA.
authors.forename: James O
authors.initials: JO
authors.lastname: Berger
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Wang
authors.forename: Lei
authors.initials: L
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D018579:Patient Selection; D057285:Precision Medicine; D012107:Research Design / Q000706:statistics & numerical data*; D016896:Treatment Outcome