1. FDA Division of Cardiovascular Devices
Bayesian Analysis and the Power Prior: FDA Division of Cardiovascular Devices Perspective
Bram Zuckerman, MD
Director,
FDA Division of Cardiovascular Devices

2. Disclosure Statement
Bram Zuckerman, MD has no relevant disclosures

3. Medical Device Trials
Complex and difficult process for all stakeholders
When appropriate need to take advantage of advances in clinical trials science
ethical
scientific
financial

4. Bayesian Power Prior Methodology
Work initiated by Ibrahim and Chen (2000) has significantly moved the Bayesian field forward
The Power Prior: Theory and Applications, Statistics in Medicine, 2015, 34, 3724 – 3749
Bayesian Methods in Clinical Trials: A Bayesian Analysis of ECOG Trials E1684 andE1690, BMC Medical Research Methodology, 2012, 12, 183
Increased understanding and development of approaches for appropriately utilizing informative priors
MDIC/Industry/FDA work has further emphasized the possible utility of this approach

5. Steps for Effectively Utilizing this Methodology in IDE submissions
Early interaction with FDA CDRH
Prospective development of a unified global approach for Data Collection (definitions, CRFs, adjudication)
Formation of “Bayesian teams” (statistician, engineer, medical officer)
Development of appropriate simulation strategy with FDA CDRH statisticians
Control of family-wise error

6. Construction of Pivotal FDA Device Trial (Other Key Considerations)
Prospective global medical device development perspective – allows for use of Bayesian power prior approach or other efficient methods of data aggregation and learning
Blinding when feasible and other steps to minimize bias
Minimization of missing data
Independent event adjudication and data safety monitoring
Alignment with CMS objectives

7. Relevance to FDA Clinical Trials Program
EAP
Pivotal Trial
Pediatric Device Development Program
FDA/CMS Alignment

8. Additional Reasons to Consider a Prospective Bayesian Approach
Sample size flexibility – ability of the trial to adapt the sample size to ongoing trial results when a plan has been prospectively agreed upon
Ability to do predictive modeling for early stopping considerations
Improved use of intermediate endpoints
Allows one to construct futility stopping rules
Joint modeling of time to event data and PRO information

9. Let’s Do It!