Presenter:
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Brad Carlin
University of Minnesota |
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Abstract |
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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 (time permitting) goes on to elucidate their use in Phase I and II clinical 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.
This course can be given in either 1-day or 2-day formats. In the latter case, ample time is available for lab sessions (the last 1/4 of each day) where the students get hands-on practice with the BUGS software on a collection of real problems that are distributed in advance. Also, the course's emphasis on clinical trials can be played up or deemphasized, depending on the number of participants who are interested in this material.
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)
- Hierarchical modeling and metaanalysis
- Adaptive borrowing of strength from historical data
- Principles of Bayesian clinical trial design: predictive probability, indifference zone, Bayesian
and frequentist operating characteristics (power, Type I error)
Adaptive clinical trial design topics:
- Rule-based designs for determining the MTD (e.g., 3+3)
- Model-based designs for determining the MTD (CRM, EWOC, TITE monitoring)
- 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
- Applications in medical device trials
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Presenter |
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| 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 135 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, 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 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.
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