A Comparison of Various Aggregation Functions in Multi-Criteria Decision Analysis for Drug Benefit-Risk Assessment

TL;DR

This paper compares different aggregation functions in multi-criteria decision analysis for drug benefit-risk assessment, finding that product and Scale Loss Score models often yield more intuitive and robust treatment recommendations than linear models.

Contribution

It introduces and empirically evaluates four utility score models, highlighting the advantages of product and Scale Loss Score models over traditional linear approaches.

Findings

Product and Scale Loss Score models produce more intuitive decisions.

These models are more robust to criteria correlation.

Linear models can lead to counter-intuitive recommendations.

Abstract

Multi-criteria decision analysis (MCDA) is a quantitative approach to the drug benefit-risk assessment (BRA) which allows for consistent comparisons by summarising all benefits and risks in a single score. The MCDA 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 BRA, 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.