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.

Quantitative decision models such as multiple criteria decision analysis (MCDA) can be used in benefitrisk assessment to formalize trade-offs between benefits and risks, providing transparency to the assessment process. There is however no well-established method for propagating uncertainty of treatment effects data through such models to provide a sense of the variability of the benefit-risk balance. Here we present a Bayesian statistical method that directly models the outcomes observed in randomized placebo-controlled trials and uses this to infer indirect comparisons between competing active treatments. The resulting treatment effects estimates are suitable for use within the MCDA setting, and it is possible to derive the distribution of the overall benefit-risk balance through Markov Chain Monte Carlo simulation. The method is illustrated using a case study of natalizumab for relapsingremitting Multiple Sclerosis.

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.

IntroductionThe power of ‘real world’ data to improve our understanding of the clinical aspects of multiple sclerosis (MS) is starting to be realised. Disease modifying therapy (DMT) use across the UK is driven by national prescribing guidelines. As such, the UK provides an ideal country in which to gather MS outcomes data. A rigorously conducted observational study with a focus on pharmacovigilance has the potential to provide important data to inform clinicians and patients while testing the reliability of estimates from pivotal trials when applied to patients in the UK.Methods and analysisThe primary aim of this study is to characterise the incidence and compare the risk of serious adverse events in people with MS treated with DMTs. The OPTIMISE:MS database enables electronic data capture and secure data transfer. Selected clinical data, clinical histories and patient-reported outcomes are collected in a harmonised fashion across sites at the time of routine clinical visits. The first patient was recruited to the study on 24 May 2019. As of January 2021, 1615 individuals have baseline data recorded; follow-up data are being captured and will be reported in due course.Ethics and disseminationThis study has ethical permission (London City and East; Ref 19/LO/0064). Potential concerns around data storage and sharing are mitigated by the separation of identifiable data from all other clinical data, and limiting access to any identifiable data. The results of this study will be disseminated via publication. Participants provide consent for anonymised data to be shared for further research use, further enhancing the value of the study.