PDF(Pubmed)
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.
摘要：
多标准决策分析是药物获益-风险评估的一种定量方法，通过将所有获益和风险汇总在一个分数中，可以进行一致的比较。多准则决策分析由几个部分组成，其中之一是效用（或损失）得分函数，它定义了收益和风险如何聚合成一个单一的数量。虽然线性效用评分是收益-风险评估中使用最广泛的方法之一，人们认识到，这可能会导致反直觉的决定，例如,建议使用极低获益或高风险的治疗方法。为了克服这个问题，分数构建的替代方法，即,产品,多元线性和规模损失得分模型，被建议。然而,到目前为止,关于这些模型隐含的差异的大多数论点都是启发式的。在这项工作中,我们考虑了四个模型来计算汇总的效用/损失分数，并在许多不同场景的广泛模拟研究中比较了它们的性能，在一个案例研究中。结果发现，与线性和多元线性模型相比，产品和规模损失得分模型在大多数情况下提供了更直观的治疗推荐决策，并且对标准中的相关性更稳健。
