Erasmus Medical Center, Erasmus University Rotterdam, Rotterdam, The Netherlands; Guizhou Medical University, Guiyang, Guizhou, China.
National University of Singapore, Singapore.
Value Health. 2018 Apr;21(4):456-461. doi: 10.1016/j.jval.2017.09.001. Epub 2017 Oct 18.
For many countries, the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L) value sets have been established to estimate health state utilities. To generate these value sets, researchers first collect values for a subset of preselected health states from a panel representing the general public, and then use a prediction algorithm to generate values for all 243 states. High prevalence of a health state in daily practice has historically been a key criterion in selecting a subset of health states as the observed set. More recently, other criteria have been suggested, especially approaches based on statistical criteria such as randomization and orthogonality.
To evaluate the validity and accuracy of both the earlier and newer criteria, in terms of prediction of values for all the health states and of the values of common health states in particular.
We used a pre-existing data set that contained visual analogue scale values from 126 students, each of whom valued all 243 EQ-5D-3L states. Then, we generated a series of designs and subsequently modeled the data with respect to each design. Some of these designs were used in the past; for example, the Measurement and Valuation of Health approach was included. Others were newly generated. The performance of different designs was evaluated in terms of the lowest root mean squared error for all health states taken together, and separately for common and rare states. Classification as common or rare was based on the frequency of the states' occurrence in three patient and population data sets pooled together (N = 5269).
The orthogonal design with 54 health states produced the lowest root mean squared errors. Over-representation of common health states in a design did not improve the estimations for these states. The published designs performed the worst, whereas the random selection designs were good on average. Nevertheless, the performance of the random selection designs showed more variance compared with orthogonal designs, because some of the former designs did not display appropriate balance.
The published designs gave rise to large estimation errors for the extrapolated EQ-5D-3L health states. The orthogonal design focusing on statistical efficiency showed its superiority. Overall, when weighing up design properties, increased statistical efficiency outweighs an increased error rate, if any, in rare health states.
对于许多国家而言，已经建立了三级欧洲五维健康量表（EQ-5D-3L）值集，以估计健康状况效用。为了生成这些值集，研究人员首先从代表普通大众的小组中收集了一组预先选定的健康状态的价值，然后使用预测算法为所有 243 个状态生成价值。在日常实践中，健康状态的高流行率历来是选择一组健康状态作为观察集的关键标准。最近，已经提出了其他标准，特别是基于随机和正交等统计标准的方法。
评估早期和较新的标准在预测所有健康状态的价值以及特别是常见健康状态的价值方面的有效性和准确性。
我们使用了一个预先存在的数据集，其中包含了 126 名学生的视觉模拟量表值，每个学生都对所有 243 个 EQ-5D-3L 状态进行了估值。然后，我们生成了一系列设计，并随后根据每个设计对数据进行建模。其中一些设计过去已经使用过，例如，包含了健康测量与评估方法。其他是新生成的。不同设计的性能是根据所有健康状态的最低均方根误差总和来评估的，对于常见和罕见状态则分别进行评估。常见或罕见的分类是基于三个患者和人群数据集（共 5269 例）中状态出现的频率来确定的。
具有 54 个健康状态的正交设计产生了最低的均方根误差。在设计中过度表示常见健康状态并不能改善对这些状态的估计。公布的设计表现最差，而随机选择设计平均表现良好。然而，随机选择设计的性能与正交设计相比显示出更多的变化，因为其中一些设计没有显示出适当的平衡。
公布的设计导致对推断的 EQ-5D-3L 健康状态的估计出现了较大的误差。侧重于统计效率的正交设计显示出了其优越性。总体而言，在权衡设计属性时，如果存在任何稀有健康状态的误差率增加，则增加统计效率将超过误差率。
