title: Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research.
abstract: Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion softwarebeanzcan facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE. Topics highlighted include shrinkage estimation, model choice, sensitivity analysis, and posterior predictive checking. A case study is presented in which we demonstrate the use of the methods discussed.
authors.affiliation: Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA.
authors.forename: Nicholas C
authors.identifier: 0000-0002-2399-7099
authors.initials: NC
authors.lastname: Henderson
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Louis
authors.affiliation: Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA.
authors.forename: Chenguang
authors.initials: C
authors.lastname: Wang
authors.affiliation: Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA.
authors.forename: Ravi
authors.initials: R
authors.lastname: Varadhan
keywords: Bayesian subgroup analysis; Heterogeneity of treatment effect; Hierarchical modeling; Personalized medicine; Precision medicine; Treatment–covariate interaction