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.

Word count: 3,668 without Acknowledgements or 4,089 with Acknowledgements Key messages Formal and transparent discussion of multiple viewpoints, interests and priorities facilitates mutual understanding of complex decision problems  Benefit-risk assessments of treatments should be undertaken in a structured way so that it is clear how a decision on the overall balance of a treatment's effects has been reached  Various structured approaches and singular methodologies/visual representations are available to support benefit-risk assessment of medicines, but so far universal agreement as to the most suitable method for structured benefit-risk assessment has been lacking  A team combining expertise from public and private institutions carried out a review of benefit-risk methods and visual representations, including application of the tools to case studies based on real regulatory scenarios  The project produced a clear set of practical recommendations for undertaking benefit-risk assessments, organised around a generic, five stage benefit-risk assessment roadmap /2007-2013) and EFPIA companies' in kind contribution.The processes described and conclusions drawn from the work presented herein relate solely to the testing of methodologies and representations for the evaluation of benefit and risk of medicines. This report neither replaces nor is intended to replace or comment on any regulatory decisions made by national regulatory agencies, nor the European Medicines Agency.The authors declare the following conflicts of interest: Dr Hughes has been employed by Pfizer Inc. for the duration of the project. Mr Downey reports that he is an employee of Amgen, a participant in the Innovative Medicines Initiative, which is a public-private partnership. The manuscript describes testing benefit-risk methodologies and visualizations using case studies of marketed products. No Amgen treatments were used in the work associated with this publication. Dr Juhaeri is an employee of Sanofi, the producer of rimonabant and telithromycin, which were used in the PROTECT project as case studies. Dr Juhaeri declares that he is an employee or Sanofi, the manufacturer of rimonabant which was studied in this project. Mr Lieftucht reports that he is an employee of GlaxoSmithKline, a participant in the Innovative Medicines Initiative, which is a public-private partnership. One of the case studies described in the manuscript is a GSK product but Mr Lieftucht did not work on that case study. Dr Metcalf reports that she is an employee of GlaxoSmithKline, a participant in the Innovative Medicines Initiative, which is a publicprivate partnership. One of the case studies described in the manuscript is a GSK product but Dr Metcalf did not work on that case study. To draw on the practical experience from the PROTECT BR case studies and make recommendations regarding the application of a number of methodologies and visual representations for benefit-risk assessment. MethodsEight case studies based on the benefit-risk balance of real medicines were ...

While benefit-risk assessment is a key component of the drug development and maintenance process, it is often described in a narrative. In contrast, structured benefit-risk assessment builds on established ideas from decision analysis and comprises a qualitative framework and quantitative methodology. We compare two such frameworks, applying multi-criteria decision-analysis (MCDA) within the PrOACT-URL framework and weighted net clinical benefit (wNCB), within the BRAT framework. These are applied to a case study of natalizumab for the treatment of relapsing remitting multiple sclerosis. We focus on the practical considerations of applying these methods and give recommendations for visual presentation of results. In the case study, we found structured benefit-risk analysis to be a useful tool for structuring, quantifying, and communicating the relative benefit and safety profiles of drugs in a transparent, rational and consistent way. The two frameworks were similar. MCDA is a generic and flexible methodology that can be used to perform a structured benefit-risk in any common context. wNCB is a special case of MCDA and is shown to be equivalent to an extension of the number needed to treat (NNT) principle. It is simpler to apply and understand than MCDA and can be applied when all outcomes are measured on a binary scale.

This study examines European decision makers' consideration and use of quantitative preference data. Methods:The study reviewed quantitative preference data usage in 31 European countries to support marketing authorization, reimbursement, or pricing decisions. Use was defined as: agency guidance on preference data use, sponsor submission of preference data, or decision-maker collection of preference data. The data could be collected from any stakeholder using any method that generated quantitative estimates of preferences. Data were collected through: (1) documentary evidence identified through a literature and regulatory websites review, and via key opinion leader outreach; and (2) a survey of staff working for agencies that support or make healthcare technology decisions.Results: Preference data utilization was identified in 22 countries and at a European level. The most prevalent use (19 countries) was citizen preferences, collected using time-trade off or standard gamble methods to inform health state utility estimation. Preference data was also used to: (1) value other impact on patients, (2) incorporate non-health factors into reimbursement decisions, and (3) estimate opportunity cost. Pilot projects were identified (6 countries and at a European level), with a focus on multi-criteria decision analysis methods and choice-based methods to elicit patient preferences. Conclusion:While quantitative preference data support reimbursement and pricing decisions in most European countries, there was no utilization evidence in European-level marketing authorization decisions. While there are commonalities, a diversity of usage was identified between jurisdictions. Pilots suggest the potential for greater use of preference data, and for alignment between decision makers.

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.