Bayesian population finding with biomarkers in a randomized clinical trial

Summary 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.