Applying Multiple Criteria Decision Analysis to Comparative Benefit-Risk Assessment Choosing among Statins in Primary Prevention

• Published in: Medical decision making Vol. 35; no. 7; pp. 859 - 871
• Main Authors: Tervonen, Tommi, Naci, Huseyin, van Valkenhoef, Gert, Ades, Anthony E., Angelis, Aris, Hillege, Hans L., Postmus, Douwe
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-10-2015
• Subjects: Decision Making; Humans; Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use; Primary Prevention; Risk Assessment; Uncertainty; multiattribute utility function; decision aids; decision analysis
• ISSN: 0272-989X; 1552-681X
• DOI: 10.1177/0272989X15587005

Decision makers in different health care settings need to weigh the benefits and harms of alternative treatment strategies. Such health care decisions include marketing authorization by regulatory agencies, practice guideline formulation by clinical groups, and treatment selection by prescribers and patients in clinical practice. Multiple criteria decision analysis (MCDA) is a family of formal methods that help make explicit the tradeoffs that decision makers accept between the benefit and risk outcomes of different treatment options. Despite the recent interest in MCDA, certain methodological aspects are poorly understood. This paper presents 7 guidelines for applying MCDA in benefit-risk assessment and illustrates their use in the selection of a statin drug for the primary prevention of cardiovascular disease. We provide guidance on the key methodological issues of how to define the decision problem, how to select a set of nonoverlapping evaluation criteria, how to synthesize and summarize the evidence, how to translate relative measures to absolute ones that permit comparisons between the criteria, how to define suitable scale ranges, how to elicit partial preference information from the decision makers, and how to incorporate uncertainty in the analysis. Our example on statins indicates that fluvastatin is likely to be the most preferred drug by our decision maker and that this result is insensitive to the amount of preference information incorporated in the analysis.