University of South Florida, 4202 E Fowler Ave, Tampa, FL, 33620, USA.
RTI Health Solutions, Manchester, UK.
Pharmacoeconomics. 2022 Oct;40(10):943-956. doi: 10.1007/s40273-022-01178-y. Epub 2022 Aug 12.
Accounting for preference heterogeneity is a growing analytical practice in health-related discrete choice experiments (DCEs). As heterogeneity may be examined from different stakeholder perspectives with different methods, identifying the breadth of these methodological approaches and understanding the differences are major steps to provide guidance on good research practices.
Our objective was to systematically summarize current practices that account for preference heterogeneity based on the published DCEs related to healthcare.
This systematic review is part of the project led by the Professional Society for Health Economics and Outcomes Research (ISPOR) health preference research special interest group. The systematic review conducted systematic searches on the PubMed, OVID, and Web of Science databases, as well as on two recently published reviews, to identify articles. The review included health-related DCE articles published between 1 January 2000 and 30 March 2020. All the included articles also presented evidence on preference heterogeneity analysis based on either explained or unexplained factors or both.
Overall, 342 of the 2202 (16%) articles met the inclusion/exclusion criteria for extraction. The trend showed that analyses of preference heterogeneity increased substantially after 2010 and that such analyses mainly examined heterogeneity due to observable or unobservable factors in individual characteristics. Heterogeneity through observable differences (i.e., explained heterogeneity) is identified among 131 (40%) of the 342 articles and included one or more interactions between an attribute variable and an observable characteristic of the respondent. To capture unobserved heterogeneity (i.e., unexplained heterogeneity), the studies largely estimated either a mixed logit (n = 205, 60%) or a latent-class logit (n = 112, 32.7%) model. Few studies (n = 38, 11%) explored scale heterogeneity or heteroskedasticity.
Providing preference heterogeneity evidence in health-related DCEs has been found as an increasingly used practice among researchers. In recent studies, controlling for unexplained preference heterogeneity has been seen as a common practice rather than explained ones (e.g., interactions), yet a lack of providing methodological details has been observed in many studies that might impact the quality of analysis. As heterogeneity can be assessed from different stakeholder perspectives with different methods, researchers should become more technically pronounced to increase confidence in the results and improve the ability of decision makers to act on the preference evidence.
在与健康相关的离散选择实验（DCE）中，考虑偏好异质性是一种日益增长的分析实践。由于异质性可能会从不同利益相关者的角度用不同的方法进行检查，因此确定这些方法的广度并理解它们之间的差异是为良好的研究实践提供指导的主要步骤。
我们的目的是系统地总结目前基于与医疗保健相关的 DCE 来考虑偏好异质性的实践。
这是由健康经济学和结果研究专业学会（ISPOR）健康偏好研究特别兴趣小组领导的项目的一部分。该系统评价在 PubMed、OVID 和 Web of Science 数据库以及最近发表的两项综述中进行了系统搜索，以确定文章。该综述包括 2000 年 1 月 1 日至 2020 年 3 月 30 日期间发表的与健康相关的 DCE 文章。所有纳入的文章还根据可解释或不可解释的因素或两者都提供了偏好异质性分析的证据。
总体而言，在 2202 篇文章中有 342 篇（16%）符合提取标准。趋势表明，2010 年后偏好异质性分析大幅增加，此类分析主要检查个体特征中可观察或不可观察因素引起的异质性。在 342 篇文章中，有 131 篇（40%）确定了通过可观察差异（即解释性异质性）的异质性，其中包括属性变量与受访者可观察特征之间的一个或多个交互作用。为了捕捉不可观察的异质性（即未解释的异质性），研究人员主要估计了混合对数模型（n=205，60%）或潜在类别对数模型（n=112，32.7%）。少数研究（n=38，11%）探讨了规模异质性或异方差性。
在与健康相关的 DCE 中提供偏好异质性证据已被发现是研究人员越来越多使用的做法。在最近的研究中，控制未解释的偏好异质性已被视为一种常见做法，而不是解释性的做法（例如交互作用），但许多研究中缺乏提供方法细节的情况已经观察到，这可能会影响分析的质量。由于异质性可以从不同利益相关者的角度用不同的方法进行检查，因此研究人员应该更加注重技术，以提高对结果的信心，并提高决策者根据偏好证据采取行动的能力。
