TITLE: Recent Development on Bayesian Clinical Trial Designs Using Historical Data
SPEAKER: Ming-Hui Chen, University of Connecticut



Chen et al. (2011) proposed a general Bayesian methodology for the design of non-inferiority clinical trials with a focus on controlling type I error and power. More recently, the posterior probability approach, the Bayesian factor approach, and the conditional borrowing approach are further developed. All of these methods are unified under the general Bayesian decision rule based framework. In addition, various fitting priors such as power priors and hierarchical priors are constructed for the incorporation of historical data. Various properties of the Bayesian methods are examined and simulation-based computational algorithms are discussed. The Bayesian methods are then applied to the design of a non-inferiority medical device clinical trial with historical data from previous trials to demonstrate superiority of the Bayesian methods in sample size reduction. This tutorial starts with a brief introduction of the Bayesian approach to inference and then provides a comprehensive review of Bayesian methods for borrowing historical information and proper use of these methods in Bayesian clinical trial designs. The tutorial will present several important applications areas in design of non-inferiority clinical trials, design of superiority clinical trials, methods for go/no-go decisions, and sequential meta-analysis design. Special topics including Monte Carlo simulation, Bayesian sample size determination, and analysis of recurrent events will also be discussed.


Speaker Bio

Dr. Ming-Hui Chen is currently Professor and Head of the Department of Statistics at the University of Connecticut (UConn). He was elected to Fellow of the International Society for Bayesian Analysis in 2016, Fellow of the Institute of Mathematical Statistics in 2007, and Fellow of the American Statistical Association in 2005. He has published over 375 statistics and biostatistics methodological and medical research papers in mainstream statistics, biostatistics, and medical journals. He has also published five books, including two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He served as President of the International Chinese Statistical Association (ICSA) in 2013, Program Chair and Publication Officer of SBSS of the American Statistical Association (ASA) and the ASA Committee on Nomination for 2016-2017 to nominate candidates for ASA President/Vice President. Currently, he serves as an Editor of Bayesian Analysis and Statistics and Its Interface and an Associate Editor of JASA, JCGS, and LIDA.


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