A Bayesian approach to subgroup identification.

Published

Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Berger, JO; Wang, X; Shen, L

Published Date

  • January 2014

Published In

Volume / Issue

  • 24 / 1

Start / End Page

  • 110 - 129

PubMed ID

  • 24392981

Pubmed Central ID

  • 24392981

Electronic International Standard Serial Number (EISSN)

  • 1520-5711

International Standard Serial Number (ISSN)

  • 1054-3406

Digital Object Identifier (DOI)

  • 10.1080/10543406.2013.856026

Language

  • eng