Loading Events

« All Events

  • This event has passed.

Bayesian Methods in Drug Development Decision Making: Challenges and Value Proposition

March 21, 2020 @ 2:00 pm - 5:00 pm

Event Navigation

A free ASCPT post-conference workshop and reception sponsored by Metrum Research Group

Date: Saturday, March 21, 2020
Time: Workshop – 2:00-4:00PM (CST), Reception – 4:00-5:00PM (CST)
Instructors: Marc R. Gastonguay & Matthew M. Riggs

Space is limited. No more than 4 attendees from a given institution or company, please.

Workshop Overview

Bayesian data analysis methods are well suited for decision making in drug development and therapeutics due to their formal inclusion of prior information and specification of inferences in terms of posterior probability distributions. These methods can be applied to the design, execution, and analysis of clinical trial data, as well as to integrate information sources for strategic drug development decision making. 

Recent publications, presentations and regulatory guidance documents highlight opportunities for the use of Bayesian methods in pediatrics, rare diseases, oncology, medical devices, and drug development decision making, among others. Nevertheless, Bayesian methods have been used only sparingly in model-informed drug development efforts. Challenges to more routine adoption of Bayesian methods may include: suitability and access to tools, increased computational requirements, lack of familiarity with the methods, functional line silos, uncertainty of regulatory position, and other considerations.

This workshop is designed to explore the value proposition of Bayesian methods in model-informed drug development decision making and to understand the challenges yet to be overcome.  Content will include a review of concepts, presentation of examples, and interactive discussion about real-world challenges faced by attendees. The planned outcome of this session will be the identification of key reasons limiting the use of Bayesian methods and a roadmap for future activities aimed to enable and advance their use in model-informed drug development.