Bayesian Meta-analysis of Safety Outcomes Using Blinded Clinical Trial Data.

Abstract

During the development of an investigational new drug, identifying potential safety risks is imperative. Early detection, as well as federal regulations, requires safety monitoring procedures during clinical trials while the data are still blinded. We introduce a Bayesian meta-analytic approach for detecting a potential elevated safety risk conferred by an investigational treatment, as compared to control, using blinded data from multiple trials. Simulation studies of our approach demonstrate good operating characteristics when some relevant prior information is available. The utility of our procedure is demonstrated on data from a real clinical trial program with informative results based on blinded data compared with unblinded data. At any time during the drug development program, our approach provides easily interpretable posterior probability statements about the risk ratio for the safety event(s) of interest.