EpiBio Seminar Series | Shirin Golchi

"Bayesian Design of Clinical Trials"

Presented by Dr. Shirin Golchi an Associate Professor at the Department of Epidemiology and Biostatistics, McGill University as a part of the EpiBio Seminar Series 2026/27.

Attend in person: PHFM 3015
Attend online: please request the Zoom link at epibio@uwo.ca 

Abstract: Design and sample size determination in clinical trials has traditionally relied on the frequentist hypothesis testing framework. Sample size determination may be performed analytically when the sampling distribution of the test statistic is known and, therefore, the power function is available in analytic form. Bayesian methods have gained popularity in the design, analysis and decision making in clinical trials. Typically, Bayesian decision procedures rely on posterior summaries whose sampling distributions are estimated via Monte Carlo simulations. A Bayesian approach toward the design of clinical trials incorporates uncertainty about the design value(s) instead of conditioning on a single value of the model parameter(s). Accounting for uncertainties in the design value(s) is particularly critical in multi-parameter models and in complex designs. A major barrier in taking a Bayesian approach at the design stage are computational challenges in evaluation of Bayesian operating characteristics. In this talk, I will present recent methodological developments that enable scalable Bayesian design in clinical trials with complex designs and multi-parameter analysis models.