Online Program

Bayesian Adaptive Methods for Clinical Trials
Scott Berry, Berry Consultants 
*Bradley Carlin, University of Minnesota 
*Peter Mueller, University of Texas MD-Anderson 


Overview: Thanks in large part to the rapid development of Markov chain Monte Carlo (MCMC) methods and software for their implementation; Bayesian methods have become ubiquitous in modern biostatistical analysis. In submissions to the U.S. FDA Center for Devices and Radiological Health, where data on new devices are often scanty but researchers typically have access to large historical databases, Bayesian methods have been in common use for over a decade and in fact were the subject of a recently-released FDA guidance document. Statisticians in earlier phases (especially Phase I oncology trials) have long appreciated Bayes' ability to get good answers quickly. Moreover, an increasing desire for adaptability in clinical trials (to react to trial knowledge as it accumulates) has also led to heightened interest in Bayesian methods.

Objective: This full-day workshop (4 consecutive sessions) introduces Bayesian methods, computing, and software, and then goes on to elucidate their use in Phase I, II, and III trials. We include descriptions of how the methods can be implemented in WinBUGS, R, and BRugs, the version of the BUGS package callable from within R. In particular, we will illustrate the different ways a Bayesian might think about power when designing a trial, and how a Bayesian procedure may e calibrated to guarantee good long-run frequentist performance (i.e., low Type I and II error rates), a subject of keen interest to the FDA.

Session 1 (8:30 am - 10:15 am): Introduction to Hierarchical Bayes Methods and Computing Bayesian inference: point and interval estimation, model choice Bayesian computing: MCMC methods; Gibbs sampler; Metropolis-Hastings algorithm Hierarchical modeling and metaanalysis Principles of Bayesian clinical trial design: predictive probability, indifference zone, Bayesian and frequentist operating characteristics (power, Type I error)

Session 2 (10:30 am – 12:15 pm): Bayesian design and analysis for Phase I studies Rule-based designs for determining the MTD (e.g., 3+3) " Model-based designs for determining the MTD (CRM, EWOC, TITE monitoring, toxicity intervals) " Dose ranging and optimal biologic dosing Efficacy and toxicity Examples and software

Session 3 (1:30 pm – 3:15 pm): Bayesian design and analysis for Phase II studies Standard designs: Phase IIA (single-arm) vs. Phase IIB (multi-arm) Predictive Probability-based methods Sequential stopping: for futility, efficacy Multi-arm designs with adaptive dose allocation Hierarchical Phase II models and examples Decision theoretic methods

Session 4 (3:30 pm – 5:15 pm): Bayesian design and analysis for Phase III studies Confirmatory trials Adaptive confirmatory trials: adaptive sample size, futility analysis, arm dropping Modeling and prediction Examples from FDA-regulated trials Seamless Phase II-III trials Multiplicity and Subset Analysis Summary and Floor Discussion