Manchester Centre for Health Economics, The University of Manchester, Manchester, UK.
UWA School of Agriculture and Environment, University of Western Australia, Crawley, WA, Australia.
Patient. 2018 Apr;11(2):167-173. doi: 10.1007/s40271-017-0282-4.
Discrete choice experiments (DCEs) are used to quantify the preferences of specified sample populations for different aspects of a good or service and are increasingly used to value interventions and services related to healthcare. Systematic reviews of healthcare DCEs have focussed on the trends over time of specific design issues and changes in the approach to analysis, with a more recent move towards consideration of a specific type of variation in preferences within the sample population, called taste heterogeneity, noting rises in the popularity of mixed logit and latent class models. Another type of variation, called scale heterogeneity, which relates to differences in the randomness of choice behaviour, may also account for some of the observed 'differences' in preference weights. The issue of scale heterogeneity becomes particularly important when comparing preferences across subgroups of the sample population as apparent differences in preferences could be due to taste and/or choice consistency. This primer aims to define and describe the relevance of scale heterogeneity in a healthcare context, and illustrate key points, with a simulated data set provided to readers in the Online appendix.
离散选择实验 (DCEs) 用于量化特定样本群体对商品或服务不同方面的偏好，并且越来越多地用于评估与医疗保健相关的干预措施和服务。医疗保健 DCE 的系统评价侧重于特定设计问题的随时间变化趋势和分析方法的变化，最近更多地关注样本群体内部偏好的特定类型变化，称为口味异质性，注意到混合对数和潜在类别模型的受欢迎程度有所上升。另一种类型的变化，称为规模异质性，与选择行为的随机性差异有关，也可能解释了一些观察到的偏好权重差异。当比较样本群体的亚组之间的偏好时，规模异质性问题变得尤为重要，因为偏好的明显差异可能是由于口味和/或选择一致性造成的。本指南旨在定义和描述医疗保健背景下规模异质性的相关性，并通过提供模拟数据集来说明要点，读者可以在在线附录中找到该数据集。
