US valuation of the EQ-5D health states - Development and testing of the D1 valuation model
US valuation of the EQ-5D health states - Development and testing of the D1 valuation model
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DOI：
10.1097/00005650-200503000-00003
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发表时间：
2005-03-01
期刊：
影响因子：
3
通讯作者：
Coons, SJ
中科院分区：
文献类型：
--
作者：
Shaw, JW;Johnson, JA;Coons, SJ
目的：EQ-5D是一种简单的、多属性的、基于偏好的健康状况测量。本文介绍了美国人口为基础的EQ-5D偏好weights.Methods的统计模型的发展：一个多阶段的概率样本，从美国成年平民noninstitutional人口。受访者使用时间权衡（TTO）方法评估了243种EQ-5D健康状态中的13种。12个州的数据被用于计量经济学建模。将TTO估值线性转换为位于区间[-1，1]上。研究方法以解释多个EQ-5D维度存在问题所引起的相互作用效应。还考虑了几种替代模型质量标准（例如，合并最小二乘、随机效应）。一种改进的分裂样本的方法被用来评估模型的预测精度。所有的统计分析都考虑到了我们的抽样design.Results固有的聚类和不成比例的选择概率：我们的EQ-5D的D1模型包括序数项，以捕获偏离完美健康的影响以及相互作用的影响。D1模型的随机效应规格产生了很好的拟合所观察到的TTO数据，与整体R-2为0.38，平均绝对误差为0.025，和7个预测误差超过0.05的绝对magnesium.Conclusions：D1模型最好的预测值为观察到的健康状态。由此产生的偏好权重估计值代表了EQ-5D在美国用于健康状况评估和经济分析的效用的显著增强。
Purpose: The EQ-5D is a brief, multiattribute, preference-based health status measure. This article describes the development of a statistical model for generating US population-based EQ-5D preference weights.Methods: A multistage probability sample was selected from the US adult civilian noninstitutional population. Respondents valued 13 of 243 EQ-5D health states using the time trade-off (TTO) method. Data for 12 states were used in econometric modeling. The TTO valuations were linearly transformed to lie on the interval [-1, 1]. Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions. Several alternative model specifications (eg, pooled least squares, random effects) also were considered. A modified split-sample approach was used to evaluate the predictive accuracy of the models. All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design.Results: Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects. A random effects specification of the D1 model yielded a good fit for the observed TTO data, with an overall R-2 of 0.38, a mean absolute error of 0.025, and 7 prediction errors exceeding 0.05 in absolute magnitude.Conclusions: The D1 model best predicts the values for observed health states. The resulting preference weight estimates represent a significant enhancement of the EQ-5D's utility for health status assessment and economic analysis in the US.