Bayesian Monitoring of Safety Signals in Blinded Clinical Trial Data

Monitoring of patient safety is an indispensable part of clinical trial planning and conduct. Proactive safety signal monitoring using blinded data in on-going clinical trials enables pharmaceutical sponsors to monitor patient safety closely while maintaining the study blind. Bayesian methods, by their nature of updating knowledge based on accumulating data and synthesis of prior knowledge, provide an excellent framework for carrying out such monitoring of safety. This short communication summarizes a straightforward Bayesian framework which can be applied to safety monitoring for one or more adverse events of special interest in real clinical trial settings. This framework is general enough to allow adaptation to a number of different Bayesian models appropriate for application to different clinical settings and types of data (such as rare events, exposure-dependent events or continuous laboratory parameters). An instructive case study is presented to demonstrate the utility of the proposed method.