College of Pharmacy, Jinan University, Guangzhou, China; Medical Psychology and Psychotherapy, Erasmus Medical Center, Rotterdam, the Netherlands.
Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
Value Health. 2019 Nov;22(11):1295-1302. doi: 10.1016/j.jval.2019.06.008. Epub 2019 Aug 26.
To construct an EQ-5D-5L value set, the EuroQol Group developed a standard protocol named EuroQol Valuation Technology (EQ-VT), prescribing the valuation of 86 health states utilizing the composite time trade-off (cTTO) approach, and subsequently modeled the observed values to yield values for all 3125 states.
A recent study demonstrated that a 25-state orthogonal design could provide as accurate predictions as the EQ-VT design applying visual analogue scale data. We aimed to test that design using time trade-off (TTO) data.
We collected TTO values utilizing EQ-VT, orthogonal, and D-efficient designs. The EQ-VT design included 86 health states distributed over 3 blocks of 30 states with some duplicates. The orthogonal and D-efficient designs each comprised 1 block of 30 states. A total of 525 university students were asked to value a random block of health states using EQ-PVT (a PowerPoint replica of EQ-VT software), which generated 100 observations per health state in all 3 designs. We modeled data by design and compared the root mean square error (RMSE) between observed and predicted values within and across the designs.
The EQ-VT design had the lowest RMSE of 0.052; the RMSEs for the orthogonal and the D-efficient designs were 0.066 and 0.063, respectively. RMSE results between designs differed for more severe health states. Some coefficients differed between designs.
Smaller designs did not lead to significant increases in prediction errors when modeling TTO data (measuring 0.01 on a utility scale). Resource-constrained countries may use small designs for valuation studies, especially when other types of preference data, such as those from discrete choice experiments, are collected and modeled jointly.
为构建 EQ-5D-5L 值集，欧洲质量群组开发了一项名为欧洲质量群组估值技术（EQ-VT）的标准方案，该方案规定利用复合时间权衡法（cTTO）对 86 种健康状态进行估值，并随后对观测值进行建模，从而为所有 3125 种状态生成相应的值。
最近的一项研究表明，25 状态正交设计可以像 EQ-VT 设计应用视觉模拟量表数据那样提供准确的预测。我们旨在使用时间权衡（TTO）数据对该设计进行测试。
我们收集了利用 EQ-VT、正交和 D-有效设计得到的 TTO 值。EQ-VT 设计包括分布在 3 个 30 状态块中的 86 种健康状态，其中一些是重复的。正交和 D-有效设计每个都包含 1 个 30 状态块。共有 525 名大学生被要求使用 EQ-PVT（EQ-VT 软件的 PowerPoint 副本）对一组随机健康状态进行估值，在所有 3 种设计中，每个健康状态都会生成 100 个观测值。我们根据设计对数据进行建模，并比较了设计内和设计间观测值和预测值之间的均方根误差（RMSE）。
EQ-VT 设计的 RMSE 最低，为 0.052；正交设计和 D-有效设计的 RMSE 分别为 0.066 和 0.063。对于更严重的健康状态，设计间的 RMSE 结果有所不同。设计间的一些系数也有所不同。
当对 TTO 数据进行建模（在效用尺度上测量为 0.01）时，较小的设计并不会导致预测误差显著增加。资源有限的国家可以在估值研究中使用较小的设计，特别是当收集和联合建模其他类型的偏好数据（例如来自离散选择实验的数据）时。
