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评估欧洲质量生活五维度量表价值评估技术(EQ-VT)设计以生成国家价值集:使用扩展设计(DEVINE)研究开发印度 EQ-5D 价值集的经验教训。

Evaluation of EuroQol Valuation Technology (EQ-VT) Designs to Generate National Value Sets: Learnings from the Development of an EQ-5D Value Set for India Using an Extended Design (DEVINE) Study.

机构信息

Postgraduate Institute of Medical Education and Research, Chandigarh, India.

Guizhou Medical University, Guiyang, People's Republic of China.

出版信息

Med Decis Making. 2023 Aug;43(6):692-703. doi: 10.1177/0272989X231180134. Epub 2023 Jul 22.

DOI:10.1177/0272989X231180134
PMID:37480281
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10422850/
Abstract

INTRODUCTION

Countries develop their EQ-5D-5L value sets using the EuroQol Valuation Technology (EQ-VT) protocol. This study aims to assess if extension in the conventional EQ-VT design can lead to development of value sets with improved precision.

METHODS

A cross-sectional survey was undertaken in a representative sample of 3,548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. A novel extended EQ-VT design was created that included 18 blocks of 10 health states, comprising 150 unique health states and 135 observations per health state. In addition to the standard EQ-VT design, which is based on 86 health states and 100 observations per health state, 3 extended designs were assessed for their predictive performance. The extended designs were created by 1) increasing the number of observations per health state in the design, 2) increasing the number of health states in the design, and 3) implementing both 1) and 2) at the same time. Subsamples of the data set were created for separate designs. The root mean squared error (RMSE) and mean absolute error (MAE) were used to measure the predictive accuracy of the conventional and extended designs.

RESULTS

The average RMSE and MAE for the standard EQ-VT design were 0.055 and 0.041, respectively, for the 150 health states. All 3 types of design extensions showed lower RMSE and MAE values as compared with the standard design and hence yielded better predictive performance. RMSE and MAE were lowest (0.051 and 0.039, respectively) for the designs that use a greater number of health states. Extending the design with inclusion of more health states was shown to improve the predictive performance even when the sample size was fixed at 1,000.

CONCLUSION

Although the standard EQ-VT design performs well, its prediction accuracy can be further improved by extending its design. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state.

HIGHLIGHTS

The EQ-5D-5L value sets are developed using the standardized EuroQol Valuation Technology (EQ-VT) protocol. This is the first study to empirically assess how much can be gained from extending the standard EQ-VT design in terms of sample size and/or health states. It not only presents useful insights into the performance of the standard design of the EQ-VT but also tests the potential extensions in the standard EQ-VT design in terms of increasing the health states to be directly valued as well as the number of observations recorded to predict the utility value of each of these health states.The study demonstrates that the standard EQ-VT design performs good, and an extension in the design of the standard EQ-VT can lead to further improvement in its performance. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state. Extending the design with inclusion of more health states marginally improves the predictive performance even when the sample size was fixed at 1,000.The findings of the study will streamline the systematic process for generating precise EQ-5D-5L value sets, thus facilitating the conduct of credible, transparent, and robust outcome valuation in health technology assessments.

摘要

简介

各国使用 EuroQol 估值技术 (EQ-VT) 协议来制定 EQ-5D-5L 值集。本研究旨在评估常规 EQ-VT 设计的扩展是否可以提高精度来开发值集。

方法

在印度的 5 个不同州,使用多阶段分层随机抽样技术,对 3548 名成年受访者进行了一项代表性横断面调查。创建了一种新的扩展 EQ-VT 设计,该设计包括 18 个 10 个健康状态的块,包含 150 个独特的健康状态和每个健康状态 135 个观察值。除了基于 86 个健康状态和每个健康状态 100 个观察值的标准 EQ-VT 设计外,还评估了 3 种扩展设计的预测性能。通过以下方式创建扩展设计:1)增加设计中每个健康状态的观察值数量,2)增加设计中的健康状态数量,以及 3)同时实施 1)和 2)。为每个单独的设计创建了数据集的子样本。根均方误差 (RMSE) 和平均绝对误差 (MAE) 用于衡量标准和扩展设计的预测准确性。

结果

对于 150 个健康状态,标准 EQ-VT 设计的平均 RMSE 和 MAE 分别为 0.055 和 0.041。与标准设计相比,所有 3 种设计扩展均显示出较低的 RMSE 和 MAE 值,因此具有更好的预测性能。使用更多健康状态的设计的 RMSE 和 MAE 最低(分别为 0.051 和 0.039)。即使样本量固定在 1000,纳入更多健康状态扩展设计也被证明可以提高预测性能。

结论

尽管标准 EQ-VT 设计表现良好,但通过扩展其设计可以进一步提高其预测准确性。在 EQ-VT 中增加更多的健康状态比增加每个健康状态的观察值更有益。

重点

EQ-5D-5L 值集是使用标准化的 EuroQol 估值技术 (EQ-VT) 协议开发的。这是第一项实证评估通过扩展标准 EQ-VT 设计在样本量和/或健康状态方面可以获得多少收益的研究。它不仅为 EQ-VT 标准设计的性能提供了有用的见解,还测试了标准 EQ-VT 设计中潜在的扩展,包括增加要直接评估的健康状态数量以及记录以预测每个健康状态的效用值的观察值数量。该研究表明,标准 EQ-VT 设计表现良好,标准 EQ-VT 设计的扩展可以进一步提高其性能。在 EQ-VT 中增加更多的健康状态比增加每个健康状态的观察值更有益。即使样本量固定在 1000,纳入更多健康状态的设计扩展也会略微提高预测性能。

研究结果将简化生成精确 EQ-5D-5L 值集的系统过程,从而促进在健康技术评估中进行可信、透明和稳健的结果估值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/c102ee58f7f5/10.1177_0272989X231180134-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/4b15d2ae9816/10.1177_0272989X231180134-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/94aaa8e9586a/10.1177_0272989X231180134-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/9fbb0d88e99c/10.1177_0272989X231180134-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/c102ee58f7f5/10.1177_0272989X231180134-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/4b15d2ae9816/10.1177_0272989X231180134-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/94aaa8e9586a/10.1177_0272989X231180134-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/9fbb0d88e99c/10.1177_0272989X231180134-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/579b/10422850/c102ee58f7f5/10.1177_0272989X231180134-fig4.jpg

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