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 Bram.zuckerman@fda.hhs.gov

Disclosure Statement Bram Zuckerman, MD has no relevant disclosures

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

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

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

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

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

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

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