TY - JOUR

T1 - A comparison of various aggregation functions in multi-criteria decision analysis for drug benefit–risk assessment

AU - Menzies, Tom

AU - Saint-Hilary, Gaelle

AU - Mozgunov, Pavel

N1 - The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 31 (5), 2022, © SAGE Publications Ltd, 2022 by SAGE Publications Ltd at the Health Sciences page: https://journals.sagepub.com/home/smma on SAGE Journals Online: http://journals.sagepub.com/

PY - 2022/5/1

Y1 - 2022/5/1

N2 - 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.

AB - 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.

KW - Health Information Management

KW - Statistics and Probability

KW - Epidemiology

U2 - 10.1177/09622802211072512

DO - 10.1177/09622802211072512

M3 - Journal article

VL - 31

SP - 899

EP - 916

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 5

ER -