Erasmus University Rotterdam, Rotterdam, The Netherlands; Guizhou Medical University, Guiyang, China.
National University of Singapore, Singapore, Singapore.
Value Health. 2019 Jan;22(1):38-44. doi: 10.1016/j.jval.2018.06.015. Epub 2018 Jul 31.
OBJECTIVE: The current five-level EQ-5D (EQ-5D-5L) valuation protocol requires the valuation of 86 states. It has been demonstrated that the selection of empirically valued health states affects the extrapolated values in three-level EQ-5D (EQ-3D-3L). In this investigation, we aim to compare the performance of the current EQ-5D-5L valuation design with other designs. STUDY DESIGN: 1603 university students participated in a valuation study using a visual analog scale (VAS) to produce values for all EQ-5D-5L states. Different designs were generated to test their prediction accuracy. METHODS: Subsamples of the dataset were used to mimic data obtained from a particular design; the remaining dataset was used as the validation set. In addition to EuroQol Group Valuation Technology (EQ-VT) design, alternative subsamples and designs were created using random, orthogonal, and "optimizing D-efficiency" sampling methods. The root mean squared error (RMSE) was used as the measure of prediction accuracy. RESULTS: The EuroQol Group Valuation Technology (EQ-VT) design showed an average RMSE of 3.44 on EQ-VAS, for all 3125 health states combined. Notably, a 25-state orthogonal design performed similarly to the EQ-VT design, with a smaller RMSE of 3.40, and was thus the most efficient design. One caveat with respect to the orthogonal design was that it did not predict the mild states well. CONCLUSIONS: Our study supports the EQ-VT design. Smaller designs were identified with similar overall prediction accuracy. It is worth investigating whether issues with misprediction of mild states can be resolved, as the use of smaller size designs would reduce the cost of the valuation of EQ-5D-5L considerably.
目的:当前的五水平 EQ-5D(EQ-5D-5L)估值方案需要对 86 种状态进行估值。已经证明,经验估值健康状态的选择会影响到三级 EQ-5D(EQ-3D-3L)的外推值。在这项研究中,我们旨在比较当前 EQ-5D-5L 估值设计与其他设计的性能。
设计:1603 名大学生参与了一项使用视觉模拟量表(VAS)对所有 EQ-5D-5L 状态进行估值的研究。生成了不同的设计来测试其预测准确性。
方法:数据集的子样本用于模拟从特定设计中获得的数据;其余数据集用作验证集。除了 EuroQol 集团估值技术(EQ-VT)设计之外,还使用随机、正交和“优化 D-效率”抽样方法创建了替代子样本和设计。均方根误差(RMSE)用作预测准确性的度量。
结果:在所有 3125 种健康状态的组合中,EuroQol 集团估值技术(EQ-VT)设计的平均 RMSE 为 3.44。值得注意的是,25 状态正交设计的表现与 EQ-VT 设计相似,RMSE 较小为 3.40,因此是最有效的设计。正交设计存在一个缺点,即它不能很好地预测轻度状态。
结论:我们的研究支持 EQ-VT 设计。还确定了具有相似整体预测准确性的较小设计。值得研究是否可以解决轻度状态预测错误的问题,因为使用较小的设计可以大大降低 EQ-5D-5L 估值的成本。
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