Peter F. Thall, Ph.D.
Deputy Chairman and Professor
Department of Biostatistics
The University of Texas M. D. Anderson Cancer Center
Houston, Texas U.S.A.

Outcome-adaptive decision-making during an ongoing experiment uses the data that become available at successive times as a basis for deciding what to do next.  This is especially useful in clinical trials, where the decisions may be what dose or treatment to give the next patient, whether to drop a treatment arm, or whether to continue or terminate the trial.  The Bayesian paradigm provides a natural basis for this process.  The posterior is updated repeatedly as new data become available, and decisions are based on posterior or predictive probabilities.  This talk will consist of several examples of oncology trials using adaptive Bayesian methods, including (1) a trial of allogeneic donor lymphocytes for treating relapsed acute leukemia patients in which adaptive randomization is used to optimize the lymphocyte infusion time of each patient, (2) a new method for dose-finding in phase I trials where the doses of two different agents used in combination are varied, and (3) a trial to determine whether Gleevec has substantive anti-disease activity in sarcoma that uses a hierarchical model to account for multiple disease subtypes.