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Quantitative Benefit-Risk Assessment in Medical Product Decision Making: A Good Practices Report of an ISPOR Task Force
Affiliation:1. Kielo Research, Zug, Switzerland;2. Erasmus School of Health Policy and Management & Erasmus Choice Modelling Center, Rotterdam, The Netherlands;3. Manchester Centre for Health Economics, School of Health Sciences, The University of Manchester, Manchester, England, UK;4. Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA;5. Global R&D Epidemiology, Janssen R&D, Titusville, NJ, USA;6. Decision Support and Analysis Staff, Office of Program and Strategic Analysis, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA;7. Evidera, London, England, UK;8. School of Health and Related Research, University of Sheffield, Sheffield, England, UK;9. European Medicines Agency, Amsterdam, The Netherlands;10. Patient Council of the Michael J. Fox Foundation for Parkinson’s Research, New York, NY, USA;11. Google, San Francisco, CA, USA
Abstract:Benefit-risk assessment is commonly conducted by drug and medical device developers and regulators, to evaluate and communicate issues around benefit-risk balance of medical products. Quantitative benefit-risk assessment (qBRA) is a set of techniques that incorporate explicit outcome weighting within a formal analysis to evaluate the benefit-risk balance. This report describes emerging good practices for the 5 main steps of developing qBRAs based on the multicriteria decision analysis process. First, research question formulation needs to identify the needs of decision makers and requirements for preference data and specify the role of external experts. Second, the formal analysis model should be developed by selecting benefit and safety endpoints while eliminating double counting and considering attribute value dependence. Third, preference elicitation method needs to be chosen, attributes framed appropriately within the elicitation instrument, and quality of the data should be evaluated. Fourth, analysis may need to normalize the preference weights, base-case and sensitivity analyses should be conducted, and the effect of preference heterogeneity analyzed. Finally, results should be communicated efficiently to decision makers and other stakeholders. In addition to detailed recommendations, we provide a checklist for reporting qBRAs developed through a Delphi process conducted with 34 experts.
Keywords:benefit-risk assessment  discrete choice experiment  multicriteria decision analysis  patient preferences  stochastic multicriteria acceptability analysis  swing weighting  threshold technique
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