A comparison of various aggregation functions in multi-criteria decision analysis for drug benefit–risk assessment. (May 2022)

• Record Type: Journal Article
• Authors: Menzies, Tom; Saint-Hilary, Gaelle; Mozgunov, Pavel
• Is Part Of: Statistical methods in medical research. Volume 31:Number 5(2022)
• Page Start: 899
• Page End: 916
• Publication Date: 2022-05
• Subjects: Aggregation function -- benefit–risk -- decision-making -- loss score -- multi-criteria decision analysis; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802211072512
• Languages: English
• ISSNs: 0962-2802

Abstract

Multi-criteria decision analysis is a quantitative approach to the drug benefit–risk assessment which allows for consistent comparisons by summarising all benefits and risks in a single score. The multi-criteria decision analysis consists of several components, one of which is the utility (or loss) score function that defines how benefits and risks are aggregated into a single quantity. While a linear utility score is one of the most widely used approach in benefit–risk assessment, it is recognised that it can result in counter-intuitive decisions, for example, recommending a treatment with extremely low benefits or high risks. To overcome this problem, alternative approaches to the scores construction, namely, product, multi-linear and Scale Loss Score models, were suggested. However, to date, the majority of arguments concerning the differences implied by these models are heuristic. In this work, we consider four models to calculate the aggregated utility/loss scores and compared their performance in an extensive simulation study over many different scenarios, and in a case study. It is found that the product and Scale Loss Score models provide more intuitive treatment recommendation decisions in the majority of scenarios compared to the linear and multi-linear models, and are more robust to the correlation in the criteria.