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

Menzies, Tom and Saint-Hilary, Gaelle and Mozgunov, Pavel (2022) A comparison of various aggregation functions in multi-criteria decision analysis for drug benefit–risk assessment. Statistical Methods in Medical Research, 31 (5). pp. 899-916. ISSN 0962-2802

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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.

Item Type:
Journal Article
Journal or Publication Title:
Statistical Methods in Medical Research
Additional Information:
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/
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
167747
Deposited By:
Deposited On:
22 Mar 2022 13:50
Refereed?:
Yes
Published?:
Published
Last Modified:
25 May 2022 11:00