DIA 2020 Virtual Global Annual Meeting

Component Type: Session
Level: Intermediate

Bayesian statistics have gained much traction over the last decade in clinical development. It can be attributed to the advancement in Bayesian modeling that is more tailored for clinical trial designs as well as the expansion of technologies that allow for much faster simulations. On the other hand, even with the inertia from both regulatory and industry to conduct traditional randomized controlled trials, it is indisputable that innovative trial designs need to be considered in the current competitive environment. Bayesian statistics provides a natural and systematic approach for trialists to leverage strength from existing data and knowledge at the design stage. It has proven to be much more efficient and just as rigorous, if not more, than traditional designs. Utilization of Bayesian statistics in clinical trials has gained much wider acceptance in clinical development, varying from pre-clinical studies to post-marketing researches, including registrational trials. Similar to other statistical tools, Bayesian statistics can be used in different designs, at different stages for different purposes. This session will have speakers from both industry and regulatory discussing various ways of using Bayesian statistics in clinical trials, introducing new Bayesian models, and assessing advantages and caveats as well as providing guidance and lessons learned.

Learning Objectives

Describe utilization of Bayesian statistics in clinical development; Discuss how Bayesian statistics can provide a more efficient trial design; Identify advantages and caveats of using Bayesian statistics in clinical trial designs, as well as regulatory guidance.

Chair

Freda W. Cooner, PhD

Speaker

Overview and Optimization of an Adaptive Bayesian Two-Stage “Drop-the-Losers” Trial Design
Alex Karanevich, PhD

Pediatric Drug Development and Bayesian Method
Hengrui Sun, DrPH, MD

Design of a Single Arm Trial in a Rare, Progressive Disease Using Natural Histories and a Disease Progression Model
Joe Marion, PhD