Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Value Health. 2022 Jul;25(7):1218-1226. doi: 10.1016/j.jval.2021.11.1370. Epub 2022 Jan 5.
This study aimed to develop the Indian 5-level version EQ-5D (EQ-5D-5L) value set, which is a key input in health technology assessment for resource allocation in healthcare.
A cross-sectional survey using the EuroQol Group's Valuation Technology was undertaken in a representative sample of 3548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. The participants were interviewed using a computer-assisted personal interviewing technique. This study adopted a novel extended EuroQol Group's Valuation Technology design that included 18 blocks of 10 composite time trade-off (c-TTO) tasks, comprising 150 unique health states, and 36 blocks of 7 discrete choice experiment (DCE) tasks, comprising 252 DCE pairs. Different models were explored for their predictive performance. Hybrid modeling approach using both c-TTO and DCE data was used to estimate the value set.
A total of 2409 interviews were included in the analysis. The hybrid heteroscedastic model with censoring at -1 combining c-TTO and DCE data yielded the most consistent results and was used for the generation of the value set. The predicted values for all 3125 health states ranged from -0.923 to 1. The preference values were most affected by the pain/discomfort dimension.
This is the largest EQ-5D-5L valuation study conducted so far in the world. The Indian EQ-5D-5L value set will promote the effective conduct of health technology assessment studies in India, thereby generating credible evidence for efficient resource use in healthcare.
本研究旨在制定印度 5 级版 EQ-5D(EQ-5D-5L)效用值集,这是医疗保健资源分配中的卫生技术评估的关键投入。
采用欧洲五维健康量表集团的估值技术,通过多阶段分层随机抽样技术,从印度 5 个不同邦中选取了 3548 名成年受访者进行横断面调查。使用计算机辅助个人访谈技术对参与者进行访谈。本研究采用了一种新颖的扩展欧洲五维健康量表集团的估值技术设计,其中包括 18 个包含 10 个组合时间权衡(c-TTO)任务的块,包含 150 个独特的健康状态,以及 36 个包含 7 个离散选择实验(DCE)任务的块,包含 252 个 DCE 对。探索了不同的模型以评估其预测性能。使用 c-TTO 和 DCE 数据的混合建模方法用于估计效用值集。
共纳入了 2409 次访谈。使用结合了 c-TTO 和 DCE 数据的异方差混合模型,并对 -1 进行了截断,得出的结果最为一致,因此用于生成效用值集。所有 3125 个健康状态的预测值范围从 -0.923 到 1。偏好值受疼痛/不适维度的影响最大。
这是迄今为止在世界范围内进行的最大的 EQ-5D-5L 估值研究。印度 EQ-5D-5L 效用值集将促进印度卫生技术评估研究的有效开展,从而为医疗保健中有效利用资源提供可信证据。