SEMINAR – April 4, 2025

Abstract

We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment where a nonterminal event may be censored by one (or more) terminal event(s), but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions (using factorizations based on vine copulas) indexed by sensitivity parameters and show how to draw inference using our BNP approach. We illustrate our methodology using data from a cardiovascular cohort study. 

About the Speaker

Mike Daniels, Sc.D., received his undergraduate degree from Brown University in Applied Math and doctoral degree from Harvard University in Biostatistics. He has been on the faculty at Iowa State and University of Texas at Austin. Currently, Dr. Daniels is Professor, Andrew Banks Family Endowed Chair, and Chair in the Department of Statistics at the University of Florida. He is a past president of ENAR. He is a fellow of the American Statistical Association, former chair of the Statistics in Epidemiology Section of the American Statistical Association (ASA), former chair of the Biometrics Section of the ASA, and former co-editor of Biometrics. He has received the Lagakos Distinguished Alumni Award from Harvard Biostatistics and the L. Adrienne Cupples Award from Boston University. He has published extensively on Bayesian methods for missing data, longitudinal data and causal inference and has been funded by U.S. NIH R01 grants as PI and/or MPI since 2001. He also has a strong and productive record of collaborative research, with a focus on behavioral trials in smoking cessation and weight management, muscular dystrophy, and HIV.

Event Organizer

David Kepplinger
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University