Bayesian Adaptive Methods for Clinical Trials
Presented by: Bradley Carlin, Ph.D
University of Minnesota, Department of Biostatistics
Date: Half-day course
Thursday, April 26, 2012
8:00 a.m. – 12:00 p.m. Central time
Location: Cerner Vision Center
Cerner Corporation’s World Headquarters Campus
2850 Rockcreek Parkway
Kansas City, Missouri 64117
Course Description:
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 regulatory agencies where data on new drugs or medical devices are often scanty but researchers have access to large historical databases, Bayesian methods have emerged as particularly helpful in combining the disparate sources of information while maintaining traditional frequentist protections regarding Type I error and power. Biostatisticians in earlier phases (especially Phase I oncology trials) have long appreciated Bayes' ability to get good answers quickly. Finally, 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. This lecture series introduces Bayesian methods, computing, and software, and then goes on to elucidate their use in Phase I and II clinical trials. We include descriptions of how the methods can be implemented in WinBUGS, R, and BRugs, a version of the BUGS package callable from within R.
Objectives:
- First session
- Hierarchical Bayes Methods and Computing for Clinical Trials
- Review of 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)
- Second session
- Topics in Bayesian design and analysis for Phase I and II 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)
- Efficacy and toxicity
- 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 randomization
Presenter:
Bradley Carlin, Ph.D
Mayo Professor in Public Health and Head of the Division of Biostatistics, University of Minnesota School of Public Health
Brad Carlin is Mayo Professor in Public Health and Professor and Head of the Division of Biostatistics at the University of Minnesota. He has published more than 125 papers in refereed books and journals, and has co-authored two popular textbooks, Bayesian Methods for Data Analysis” with Tom Louis and “Hierarchical Modeling and Analysis for Spatial Data” with Sudipto Banerjee and Alan Gelfand, as well as a recently-completed third project, "Bayesian Adaptive Methods for Clinical Trials" with Scott Berry, Peter Muller, and J. Jack Lee. He is a winner of the Mortimer Spiegelman Award from the APHA, and from 2006-2009 served as editor-in-chief of Bayesian Analysis, the official journal of the International Society for Bayesian Analysis (ISBA). Prof. Carlin has extensive experience teaching short courses and tutorials, and recently won a teaching award at University of Minnesota. During his spare time, Brad is a musician and bandleader, providing keyboards and vocals in a variety of venues, some of the more interesting of which are visible by typing the phrase "Bayesian cabaret" into the search window at YouTube.
Registration:
Click on the Registration link at the left-hand side of this page for fee information and to register.