Benefit-Risk Assessment of Medical Products Using Bayesian Multi-Criteria Augmented Decision Analysis (MCADA) for Clinical Development

Abstract

AbstractMulti-criteria decision analysis is a benefit-risk assessment tool that evaluates multiple competing benefit and risk criteria simultaneously. This has the potential to aid sponsors in making effective and informed go/no-go decisions for their clinical development program. This method involves assigning weights to various benefit and risk criteria based on their relative importance (utility weight) and summing them to compute a single utility score that represents the overall benefit-risk profile of the treatment. However, this approach is constrained to binary and continuous parameters. In this paper, we introduce a novel framework known as Bayesian Multi-Criteria Augmented Decision Analysis (MCADA), which extends existing methods to encompass time-to-event and ordinal outcomes while incorporating linear and novel non-linear functions in utility aggregation. This paper provides a comprehensive description of the statistical methodology behind the MCADA framework and demonstrates its application using IPD and aggregate data from two clinical trials. Our two case studies show that the MCADA framework can be effectively used to produce a single utility score that reflects the overall benefit-risk profile of the treatment using both IPD and aggregate data from trials. MCADA broadens the horizon of the existing MCDA framework by accommodating a wider range of data types and utility functions in the utility aggregation process.