Methodologies to help benefit-risk assessments of medicines are diverse and each is associated with different limitations and strengths. There is not a 'one-size-fits-all' method, and a combination of methods may be needed for each benefit-risk assessment. The taxonomy introduced herein may guide choice of adequate methodologies. Finally, we recommend 13 of 49 methodologies for further appraisal for use in the real-life benefit-risk assessment of medicines.

Purpose Difficulties may be encountered when undertaking a benefit–risk assessment for an older product with well‐established use but with a benefit–risk balance that may have changed over time. This case study investigates this specific situation by applying a formal benefit–risk framework to assess the benefit–risk balance of warfarin for primary prevention of patients with atrial fibrillation. Methods We used the qualitative framework BRAT as the starting point of the benefit–risk analysis, bringing together the relevant available evidence. We explored the use of a quantitative method (stochastic multi‐criteria acceptability analysis) to demonstrate how uncertainties and preferences on multiple criteria can be integrated into a single measure to reduce cognitive burden and increase transparency in decision making. Results Our benefit–risk model found that warfarin is favourable compared with placebo for the primary prevention of stroke in patients with atrial fibrillation. This favourable benefit–risk balance is fairly robust to differences in preferences. The probability of a favourable benefit–risk for warfarin against placebo is high (0.99) in our model despite the high uncertainty of randomised clinical trial data. In this case study, we identified major challenges related to the identification of relevant benefit–risk criteria and taking into account the diversity and quality of evidence available to inform the benefit–risk assessment. Conclusion The main challenges in applying formal methods for medical benefit–risk assessment for a marketed drug are related to outcome definitions and data availability. Data exist from many different sources (both randomised clinical trials and observational studies), and the variability in the studies is large. Copyright © 2014 John Wiley & Sons, Ltd.

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