title: Bayesian population finding with biomarkers in a randomized clinical trial.
abstract: The identification of good predictive biomarkers allows investigators to optimize the target population for a new treatment. We propose a novel utility-based Bayesian population finding (BaPoFi) method to analyze data from a randomized clinical trial with the aim of finding a sensitive patient population. Our approach is based on casting the population finding process as a formal decision problem together with a flexible probability model, Bayesian additive regression trees (BART), to summarize observed data. The proposed method evaluates enhanced treatment effects in patient subpopulations based on counter-factual modeling of responses to new treatment and control for each patient. In extensive simulation studies, we examine the operating characteristics of the proposed method. We compare with a Bayesian regression-based method that implements shrinkage estimates of subgroup-specific treatment effects. For illustration, we apply the proposed method to data from a randomized clinical trial.
authors.affiliation: Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Morita
authors.affiliation: Department of Mathematics, University of Texas, Austin, Texas, U.S.A.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
mesh_terms: D001499:Bayes Theorem; D015415:Biomarkers; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic
chemical_list: D015415:Biomarkers
keywords: Bayesian additive regression trees; Bayesian decision problem; Biomarkers; Population finding; Randomized clinical trial