BSWG KOL Lecture Series, L28

Event Date:



Use of Bayesian Hierarchical Models in the Presentation of Subgroup Analyses

Mark Rothmann PhD
Director of the Division of Biometrics II, FDA

Abstract: Sample estimates of treatment effects across subgroups tend to have more variability relative to the true underlying treatment effects, affecting interpretation. Shrinkage estimation through Bayesian Hierarchical Models addresses this unwanted variation. In a one-way hierarchical structure, the estimated treatment effects across subgroups have posterior means that shrink the sample estimates toward the overall estimate. The amount of shrinkage increases as the variation between relative to within the subgroups decreases. Because the shrinkage estimates borrow strength from the overall estimate, there is greater precision than separate analyses of the sample treatment effects. This approach considers the relevancy of data outside a subgroup of interest when estimating that subgroup’s treatment effect.

Bio: Dr. Rothmann (Mark) is the Director of the Division of Biometrics II. He earned his Ph. D. in Statistics at the University of Iowa in 1990. He then spent the next nine years as a professor at various universities before coming to the FDA in 1999. At the FDA, when he was a reviewer and team leader he was involved in the review on Oncology, Hematology, and Metabolism and Endocrinology products. He has done research in several areas involving the design and analysis of clinical trials and co-authored the book Design and Analysis of Non-Inferiority Trials. He currently leads three Office of Biostatistics Working Groups and Committees in Bayesian Analysis, Drug Trials Snapshots and Pediatric Studies and has served on many working groups and committees.

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