School of Health and Related Research (ScHARR), University of Sheffield, 30 Regent St, Sheffield City Centre, Sheffield, S1 4DA, UK.
Health Policy Research Unit, Shenzhen Health Development Research and Data Management Center, Shenzhen, Guangdong Province, China.
Appl Health Econ Health Policy. 2023 May;21(3):405-418. doi: 10.1007/s40258-023-00794-9. Epub 2023 Mar 30.
Discrete choice experiments (DCEs) are increasingly used in health state valuation studies.
This systematic review updates the progress and new findings of DCE studies in the health state valuation, covering the period since the review of June 2018 to November 2022. The review reports the methods that are currently being used in DCE studies to value health and study design characteristics, and, for the first time, reviews DCE health state valuation studies published in the Chinese language.
English language databases PubMed and Cochrane, and Chinese language databases Wanfang and CNKI were searched using the self-developed search terms. Health state valuation or methodology study papers were included if the study used DCE data to generate a value set for a preference-based measure. Key information extracted included DCE study design strategies applied, methods for anchoring the latent coefficient on to a 0-1 QALY scale and data analysis methods.
Sixty-five studies were included; one Chinese language publication and 64 English language publications. The number of health state valuation studies using DCE has rapidly increased in recent years and these have been conducted in more countries than prior to 2018. Wide usage of DCE with duration attributes, D-efficient design and models accounting for heterogeneity has continued in recent years. Although more methodological consensus has been found than in studies conducted prior to 2018, this consensus may be driven by valuation studies for common measures with an international protocol (the 'model' valuation research). Valuing long measures with well-being attributes attracted attention and more realistic design strategies (e.g., inconstant time preference, efficient design and implausible states design) were identified. However, more qualitative and quantitative methodology study is still necessary to evaluate the effect of those new methods.
The use of DCEs in health state valuation continues to grow dramatically and the methodology progress makes the method more reliable and pragmatic. However, study design is driven by international protocols and method selection is not always justified. There is no gold standard for DCE design, presentation format or anchoring method. More qualitative and quantitative methodology study is recommended to evaluate the effect of new methods before researchers make methodology decisions.
离散选择实验（DCE）越来越多地用于健康状态估值研究。
本系统综述更新了自 2018 年 6 月综述以来至 2022 年 11 月期间 DCE 研究在健康状态估值中的进展和新发现。该综述报告了目前用于 DCE 研究以对健康进行估值的方法和研究设计特征，并且首次综述了以中文发表的 DCE 健康状态估值研究。
使用自行开发的检索词，检索英文数据库 PubMed 和 Cochrane，以及中文数据库万方和中国知网，纳入使用 DCE 数据为偏好测量生成价值集的健康状态估值或方法学研究论文。提取的关键信息包括应用的 DCE 研究设计策略、将潜在系数锚定到 0-1 QALY 尺度的方法和数据分析方法。
共纳入 65 项研究，包括 1 篇中文出版物和 64 篇英文出版物。近年来，使用 DCE 进行健康状态估值的研究数量迅速增加，且研究国家多于 2018 年之前。近年来，DCE 与持续时间属性、D 有效设计和异质性模型的广泛使用仍在继续。尽管与 2018 年之前的研究相比，发现了更多的方法共识，但这种共识可能是由具有国际协议（“模型”估值研究）的常见测量值的估值研究驱动的。对具有幸福感属性的长测量值进行估值引起了关注，并确定了更现实的设计策略（例如，不变时间偏好、有效设计和不合理状态设计）。然而，仍需要更多的定性和定量方法学研究来评估这些新方法的效果。
DCE 在健康状态估值中的应用继续迅速增长，方法学进展使该方法更加可靠和实用。然而，研究设计受国际协议驱动，方法选择并非总是合理的。DCE 设计、呈现格式或锚定方法没有黄金标准。建议进行更多的定性和定量方法学研究，以在研究人员做出方法学决策之前评估新方法的效果。
