Bayesian Analysis and the Power
Prior:
FDA Division of Cardiovascular
Devices Perspective
Bram Zuckerman, MD
Director,
FDA Division of Cardiovascular Devices
[email protected]
Disclosure Statement
Bram Zuckerman, MD has no relevant
disclosures
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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
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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
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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
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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
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Relevance to FDA Clinical Trials
Program
• EAP
• Pivotal Trial
• Pediatric Device Development Program
• FDA/CMS Alignment
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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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Let’s Do It!
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