This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
|CE_19C||Tue, 8/3/2010, 8:30 AM - 5:00 PM||CC-1 (East)|
|Bayesian Adaptive Methods for Clinical Trials — Continuing Education Course|
|Section on Bayesian Statistical Science|
|Instructor(s): Bradley P. Carlin, University of Minnesota, Donald Arthur Berry, MD Anderson Cancer Center, Scott Berry, Berry Consultants, LLC, J. Jack Lee, MD Anderson Cancer Center|
|Thanks in large part to the rapid development of MCMC computing, Bayesian methods have become ubiquitous in modern biostatistical analysis. In submissions to the U.S. FDA Center for Devices and Radiological Health, 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 has also led to heightened interest in Bayesian methods. This course introduces hierarchical Bayes methods for the design, interim monitoring, and analysis of clinical trials data, and demonstrates their usefulness in challenging applied settings. Methods appropriate for Phases I, II, and III of the American regulatory system will be covered. We will also provide illustrations using the R, WinBUGS, and BRugs software packages. Short course participants should have an M.S. (or advanced undergraduate) understanding of mathematical statistics at, say, the Casella and Berger (2001) level, applied Bayesian methods at, say, the Carlin and Louis (2009) or Gelman et al. (2004) levels. We will not assume any significant previous experience with Bayesian data analysis or computing, although students with basic knowledge of these areas will certainly face a gentler learning curve.|