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将死亡率纳入健康效用衡量指标中。

Incorporating Mortality in Health Utility Measures.

机构信息

Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA.

Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, ON, Canada.

出版信息

Med Decis Making. 2020 Oct;40(7):862-872. doi: 10.1177/0272989X20951778. Epub 2020 Sep 30.

Abstract

The creation of multiattribute health utility systems requires design choices that have profound effects on the utility model, many of which have been documented and studied in the literature. Here we describe one design choice that has, to the best of our knowledge, been unrecognized and therefore ignored. It can emerge in any multiattribute decision analysis in which one or more essential outcomes cannot be described in terms of the multiattribute space. In health applications, the state of being dead is such an outcome. When the remaining health is conceptualized as a multidimensional space, determining the utility of the state of being dead requires using the interval-scale properties of cardinal utility, combined with elicited utilities for the state of being dead and the all-worst state, to produce a utility function in which the state of being dead has a utility of 0 and full health has a utility of 1 (i.e., the quality-adjusted life-year scale). Although previously unrecognized, there are two approaches to accomplish that step, and they produce different results in almost all cases. As a corollary, the choice of approach determines the proportion of states rated as worse than dead by the system. For example, in the Health Utility Index 3 (HUI3), the method used classifies 78% of the 972,000 unique health states in the classification system as worse than dead, and that proportion increases to 85% when the HUI3 is recalculated using the alternative approach. Studies of populations with significant morbidity are the most likely to be sensitive to the design choice. Those who design utility measures should be aware that they are using a researcher degree of freedom when they decide how to scale the state of being dead.

摘要

多属性健康效用系统的创建需要进行设计选择,这些选择会对效用模型产生深远影响,其中许多选择已经在文献中得到了记录和研究。在这里,我们描述了一个设计选择,据我们所知,这个选择尚未被认识到,因此被忽略了。它可能出现在任何多属性决策分析中,其中一个或多个基本结果无法用多属性空间来描述。在健康应用中,死亡状态就是这样一个结果。当剩余的健康被概念化为多维空间时,确定死亡状态的效用需要使用基数效用的区间尺度属性,结合对死亡状态和所有最差状态的诱发效用,来产生一个效用函数,其中死亡状态的效用为 0,完全健康的效用为 1(即质量调整生命年尺度)。尽管以前没有被认识到,但有两种方法可以完成这一步骤,而且它们在几乎所有情况下都会产生不同的结果。作为推论,方法的选择决定了系统中被评为比死亡更差的状态的比例。例如,在健康效用指数 3(HUI3)中,所使用的方法将分类系统中 972000 个独特健康状态中的 78%归类为比死亡更差,而当使用替代方法重新计算 HUI3 时,该比例增加到 85%。患有重大疾病的人群的研究最有可能对设计选择敏感。设计效用衡量标准的人应该意识到,当他们决定如何对死亡状态进行缩放时,他们正在使用研究人员的自由度。

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