A0575
Title: Bayesian population finding in a randomized clinical trial
Authors: Satoshi Morita - Kyoto University Graduate School of Medicine (Japan) [presenting]
Peter Mueller - UT Austin (United States)
Hiroyasu Abe - Kyoto University (Japan)
Abstract: A utility-based Bayesian approach is discussed to population finding in the context of randomized clinical trial (RCT). The 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. We define a utility function that addresses the competing aims of the desired report so that the decision is constrained to be parsimonious and interpretable. We illustrate the approach with a joint time-to-event and toxicity outcome from an RCT for locally advanced or metastatic breast cancer.
