Journal
BIOMETRICS
Volume 73, Issue 4, Pages 1355-1365Publisher
WILEY
DOI: 10.1111/biom.12677
Keywords
Bayesian additive regression trees; Bayesian decision problem; Biomarkers; Population finding; Randomized clinical trial
Funding
- Ministry of Health, Labour and Welfare of Japan [C-24500345]
- NIH/NCI [1 R01 CA207195-01]
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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.
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