title: Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree.
abstract: Characterization of a subpopulation by the difference in marginal means of the outcome under the intervention and control may not be sufficient to provide informative guidance for individual decision and public policy making. Specifically, often we are interested in the treatment benefit rate (TBR), that is, the probability of benefitting an intervention in a meaningful way. For binary outcomes, TBR is the proportion that has "unfavorable" outcome under the control and "favorable" outcome under the intervention. Identification of subpopulations with distinct TBR by baseline characteristics will have significant implications in clinical setting where a medical intervention with potential negative health impact is under consideration for a given patient. In addition, these subpopulations with unique TBR set the basis for guidance in implementing the intervention toward a more personalized scheme of treatment. In this article, we propose a Bayesian tree based latent variable model to seek subpopulations with distinct TBR. Our method offers a nonparametric Bayesian framework that accounts for the uncertainty in estimating potential outcomes and allows more exhaustive search of the partitions of the baseline covariates space. The method is evaluated through a simulation study and applied to a randomized clinical trial of implantable cardioverter defibrillators to reduce mortality.
authors.affiliation: Department of Biostatistics, School of Medicine, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, 46202, USA. chashen@iupui.edu.
authors.forename: Changyu
authors.initials: C
authors.lastname: Shen
authors.affiliation: School of Life Science and Technology, Harbin Institute of Technology, Harbin, HeiLongJiang, 150001, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Biostatistics, School of Medicine, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, 46202, USA.
authors.forename: Xiaochun
authors.initials: X
authors.lastname: Li
authors.affiliation: School of Life Science and Technology, Harbin Institute of Technology, Harbin, HeiLongJiang, 150001, China.
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Division of Cardiology, Krannert Institute of Cardiology, Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
authors.forename: Peng-Sheng
authors.initials: PS
authors.lastname: Chen
authors.affiliation: Clinical Electrophysiology Laboratory, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
authors.forename: Alfred E
authors.initials: AE
authors.lastname: Buxton
mesh_terms: D001145:Arrhythmias, Cardiac / Q000401:mortality / Q000601:surgery; D001499:Bayes Theorem; D003198:Computer Simulation; D017147:Defibrillators, Implantable / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D018579:Patient Selection; D057285:Precision Medicine / Q000379:methods*; D011336:Probability; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D016896:Treatment Outcome
keywords: Bayesian analysis; Causal inference; Heterogeneity of treatment effect; Matching; Subgroup analysis