Bayesian adaptive trials have gained popularity as flexible alternatives to conventional randomized clinical trial designs. Bayesian probability statements are commonly used as the basis of decision making throughout the trial to make interim adjustments and facilitate early stopping. Statistical design of Bayesian adaptive trials often requires extensive simulation studies to assess and report frequentist operating characteristics (power and type I error rate) required by regulatory agencies. This webinar will cover a brief overview of Bayesian adaptive designs for clinical trials, example trials with Bayesian adaptive designs, review of statistical procedures routinely used to assess the operating characteristics of these designs and methods for efficient estimation and uncertainty quantification for the operating characteristics.
Shirin Golchi is an assistant professor in biostatistics at the Department of Epidemiology and Biostatistics, McGill University. She graduated from Simon Fraser University with a PhD in statistics in 2014. Her research interests are Bayesian inference and computational methods with a current focus on Bayesian adaptive clinical trials.
Brad is a statistical researcher, methodologist, consultant, and instructor. Prior to joining PharmaLex, he spent 27 years on the faculty of the Division of Biostatistics at the University of Minnesota School of Public Health, serving as division head for 7 of those years. He has published more than 185 papers in refereed books and journals, and has co-authored three popular textbooks on Bayesian statistical methods and their applications in spatial statistics and adaptive clinical trials. During his spare time, Brad is a health musician and bandleader, providing keyboards and vocals in a variety of venues. He is excited and proud to be a part of the PharmaLex data science and statistics team.