American University of Beirut, Beirut, Lebanon.
Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences, American University of Beirut, Riad El Solh 1107-2020, P.O. BOX: 11-0236, Beirut, Lebanon.
Qual Life Res. 2018 Nov;27(11):2841-2850. doi: 10.1007/s11136-018-1935-z. Epub 2018 Jul 14.
Conventionally, models used for health state valuation data have been parametric. Recently, a number of researchers have investigated the use of non-parametric Bayesian methods in this area.
In this paper, we present a non-parametric Bayesian model to estimate a preference-based index for a five-dimensional health state classification, namely EQ-5D.
A sample of 2997 members of the UK general population valued 43 health states selected from a total of 243 health states defined by the EQ-5D using time trade-off technique. Findings from non-parametric modelling are reported in this paper and compared to previously used parametric estimations. The impact of respondent characteristics on health state valuations is also reported.
The non-parametric models were found to be better at predicting scores in populations with different distributions of characteristics than observed in the survey sample. Additionally, non-parametric models were found to be better at allowing for the impact of respondent characteristics to vary by health state. The results show an important age effect with sex having some effect.
The non-parametric Bayesian models provide more realistic and better utility estimates from the EQ-5D than previously used parametric models have done. Furthermore, the model is more flexible in estimating the impact of covariates.
传统上，用于健康状态估值数据的模型都是参数模型。最近，许多研究人员已经在这一领域研究了非参数贝叶斯方法的使用。
本文提出了一种非参数贝叶斯模型，用于估计五维健康状态分类（即 EQ-5D）的偏好指数。
使用时间权衡技术，从 EQ-5D 定义的总共 243 个健康状态中选择了 2997 名英国普通人群的样本，对其中的 43 个健康状态进行了评估。本文报告了非参数建模的结果，并与以前使用的参数估计进行了比较。还报告了受访者特征对健康状态估值的影响。
非参数模型在预测具有不同特征分布的人群的得分方面表现优于观察到的调查样本。此外，非参数模型更能允许受访者特征对健康状态的影响有所不同。结果显示出重要的年龄效应，性别也有一定影响。
非参数贝叶斯模型比以前使用的参数模型提供了更真实和更好的 EQ-5D 效用估计。此外，该模型在估计协变量的影响方面更具灵活性。
