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

SUBMITTER: Morita S 

PROVIDER: S-EPMC5582025 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature