Satoshi Morita, of the Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine; Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital (Kyoto, Japan), will present:
“Bayesian Population Finding in a Randomized Clinical Trial”
Abstract: We discuss a utility-based Bayesian approach to population finding in a 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. In contrast, the decision is constrained to be parsimonious and interpretable. We define a utility function that addresses the competing aims of the desired report. We illustrate the approach with a joint time-to-event and toxicity outcome from an RCT for locally advanced or metastatic breast cancer.
All are welcome.