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首页> 外文期刊>Journal of the Royal Statistical Society. Series C, Applied statistics >Estimating utilities from individual health preference data: a nonparametric Bayesian method
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Estimating utilities from individual health preference data: a nonparametric Bayesian method

机译：根据个人健康偏好数据估算效用：一种非参数贝叶斯方法

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摘要

A fundamental benefit that is conferred by medical treatments is to increase the health-related quality of life (HRQOL) that is experienced by patients. Various descriptive systems exist to define a patient's health state, and we address the problem of assigning an HRQOL value to any given state in such a descriptive system. Data derive from experiments in which individuals are asked to assign their personal values to various health states. We construct a Bayesian model that takes account of various important aspects of such data. Specifically, we allow for the repeated measures feature that each individual values several different states, and the fact that individuals vary markedly in their valuations, with some people consistently providing higher valuations than others. We model the relationship between HRQOL and health state nonparametrically. We illustrate our method by using data from an experiment in which 611 individuals each valued up to six states in the descriptive system known as the SF-6D system. Although the SF-6D system distinguishes 18000 different health states, only 249 of these were valued in this experiment. We provide posterior inference about the HRQOL values for all 18000 states.
机译：药物治疗所带来的基本好处是可以提高患者体验到的健康相关生活质量（HRQOL）。存在各种定义系统来定义患者的健康状况，并且我们解决了在这种描述系统中将HRQOL值分配给任何给定状态的问题。数据来自实验，在实验中，要求个人将其个人价值观分配给各种健康状况。我们构建了一个贝叶斯模型，该模型考虑了此类数据的各个重要方面。具体来说，我们考虑到重复测量的特征，即每个人都对几个不同的州进行估价，并且每个人的估值存在显着差异的事实，有些人始终提供比其他人更高的估值。我们非参数地建模HRQOL和健康状态之间的关系。我们通过使用来自实验的数据来说明我们的方法，在该实验中，在称为SF-6D系统的描述性系统中，每个人最多评价六个状态。尽管SF-6D系统可区分18000种不同的健康状态，但在本实验中仅对其中249种进行了评估。我们提供了关于所有18000个州的HRQOL值的后验推论。

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