  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.
