Bayesian Methods and Computing for Data Analysis and Adaptive Clinical Trials

Short Course February 18th 2014

Presented by
Bradley Carlin PhD
Professor and Division Head, Division of Biostatistics
University of Minnesota School of Public Health

About the Course

Course 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 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 the ability to react to trial knowledge as it accumulates and the ability 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:

Introduction to Bayesian inference: point and interval estimation, model choice Bayesian computing: MCMC methods, Gibbs sampler, Metropolis-Hastings algorithm, recent developments (including non-MCMC methods such as INLA)

Principles of Bayesian clinical trial design:

Predictive probability, indifference zone, Bayesian and frequentist operating characteristics (power, Type I error)

Clinical trial design and analysis topics:

Rule-based designs for determining the maximum tolerated dose (MTD), such as 3+3 Model-based designs for determining the MTD (CRM, EWOC) Sequential stopping: for futility, efficacy Multi-arm designs with adaptive randomization Adaptive borrowing of strength from historical data Applications in medical device and post-market surveillance studies Hierarchical modeling, meta-analysis, and Bayesian methods for mixed treatment comparisons (MTCs) in safety and efficacy studies
Course Details

About the Instructor

Dr. 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 he 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 APHA, and from 2006-2009 served as editor-in-chief of Bayesian Analysis, the official journal of the International Society for Bayesian Analysis (ISBA). Professor 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, Dr. Carlin 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 on YouTube.
Student Life Center
BOT room
Vanderbilt Campus
Nashville, TN 37232


9:00AM-10:20AMMorning Session I
10:40AM-12:00PMMorning Session II
1:00PM-2:30PMAfternoon Session I
2:30PM-2:50PM Break
2:50PM-4:00PMAfternoon Session II


Helpful Materials
  • Highly Recommended Materials
    • (“BCLM”): Bayesian Adaptive Methods for Clinical Trials (ISBN 978-1-4398-2548-8) by S.M. Berry, B.P. Carlin, J.J. Lee, and P. Müller, Boca Raton, FL: Chapman and Hall/CRC Press, 2010.
  • Other Suggested Materials
    • Your favorite math stat and linear models books
    • (“C&L”): Bayesian Methods for Data Analysis, 3rd ed., by B.P. Carlin and T.A. Louis, Boca Raton, FL: 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: Chichester: Wiley, 2004.
  • Laptops not required

Topic revision: r7 - 07 Feb 2014, NanaKwarteng

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