All Times EDT
Keywords: Bayesian, benefit-risk assessment
Bayesian methods enable incorporation of the uncertainty in a principled manner and thus facilitate robust decision making. Moreover, Bayesian methods with good frequentist properties capitalize on both frequentist and Bayesian paradigms. This may be leveraged in regulated environment such as drug discovery and development.
In our presentation we will consider two approaches that are germane to the Bayesian analysis and benefit-risk assessment in clinical trials. First, we will elucidate the added value of the Bayesian approach in incorporating the decision maker uncertainty into multicriteria decision analysis (MCDA) to allow better informed decisions. Then we will show how a Bayesian approach with good frequentist properties can be leveraged in benefitting subgroup identification for a variety of commonly encountered endpoints including continuous, binary, survival endpoints and counts. Finally, we will discuss potential extensions for benefit-risk analysis.