Cleveland Chapter of the ASATwenty-eighth seminar in a series on
Tools for Regaining the Competitive Edge
Dedicated to the memory of
Friday, April 11, 2014
Bayesian Methods and Computing for Data Analysis and Adaptive Clinical Trials
A One-Day Seminar Taught by
Bradley P. Carlin, Ph.D
About the Seminar: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 (to react to trial knowledge as it accumulates) and to combine results across multiple trials has also led to heightened interest in Bayesian methods.
This one-day short course introduces Bayesian methods, computing, and software, and goes on to elucidate their use in Phase I and II clinical trials, as well as meta-analysis of current and historical trials. We include descriptions and live demonstrations of how the methods can be implemented in BUGS, R, and versions of the BUGS package callable from within R.
Core Bayesian topics:
Clinical trial design and analysis topics:
The presentation will be based on the following three books:
Bayesian Adaptive Methods for Clinical Trials
by S.M. Berry, B.P. Carlin, J.J. Lee, and P. Müller, Chapman and Hall/CRC Press, 2010
Bayesian Methods for Data Analysis, 3rd ed.
by B.P. Carlin and T.A. Louis, Chapman and Hall/CRC Press, 2009
Bayesian Approaches to Clinical Trials and Health-Care Evaluation
by D.J. Spiegelhalter, K.R. Abrams, and J.P. Myles, Wiley, 2004
About the Instructor
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 150 papers in refereed books and journals, and has co-authored three popular textbooks: "Bayesian Methods for Data Analysis" with Tom Louis, "Hierarchical Modeling and Analysis for Spatial Data" with Sudipto Banerjee and Alan Gelfand, and "Bayesian Adaptive Methods for Clinical Trials" with Scott Berry, J. Jack Lee, and Peter Muller.
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). He has received uninterrupted NIH support as PI for his methodological work since 1992.
Prof. Carlin has extensive experience teaching short courses and tutorials, and has won both teaching and mentoring awards from the 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.
Location and Time:
Agenda for the Day
Full participation in this seminar can result in Recertification Units (R.U.s) earned towards maintenance of ASQ certification requirements.