Design of Discrete Choice Experiments

D. Street, R. Viney
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引用次数: 1

Abstract

Discrete choice experiments are a popular stated preference tool in health economics and have been used to address policy questions, establish consumer preferences for health and healthcare, and value health states, among other applications. They are particularly useful when revealed preference data are not available. Most commonly in choice experiments respondents are presented with a situation in which a choice must be made and with a a set of possible options. The options are described by a number of attributes, each of which takes a particular level for each option. The set of possible options is called a “choice set,” and a set of choice sets comprises the choice experiment. The attributes and levels are chosen by the analyst to allow modeling of the underlying preferences of respondents. Respondents are assumed to make utility-maximizing decisions, and the goal of the choice experiment is to estimate how the attribute levels affect the utility of the individual. Utility is assumed to have a systematic component (related to the attributes and levels) and a random component (which may relate to unobserved determinants of utility, individual characteristics or random variation in choices), and an assumption must be made about the distribution of the random component. The structure of the set of choice sets, from the universe of possible choice sets represented by the attributes and levels, that is shown to respondents determines which models can be fitted to the observed choice data and how accurately the effect of the attribute levels can be estimated. Important structural issues include the number of options in each choice set and whether or not options in the same choice set have common attribute levels. Two broad approaches to constructing the set of choice sets that make up a DCE exist—theoretical and algorithmic—and no consensus exists about which approach consistently delivers better designs, although simulation studies and in-field comparisons of designs constructed by both approaches exist.
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离散选择实验的设计
离散选择实验是健康经济学中流行的陈述偏好工具,已被用于解决政策问题,建立消费者对健康和医疗保健的偏好,以及评估健康状态等应用。当没有显示的偏好数据时,它们特别有用。在选择实验中最常见的是,被调查者必须在一种情况下做出选择,并有一组可能的选择。选项由许多属性描述,每个属性对每个选项都具有特定级别。这组可能的选项被称为“选择集”,一组选择集组成了选择实验。属性和级别由分析人员选择,以允许对应答者的潜在偏好进行建模。假设受访者做出效用最大化的决策,选择实验的目标是估计属性水平如何影响个人的效用。假设效用有一个系统成分(与属性和水平有关)和一个随机成分(可能与效用的未观察到的决定因素、个体特征或选择的随机变化有关),并且必须对随机成分的分布做出假设。从属性和级别表示的可能的选择集的范围中,选择集的结构决定了哪些模型可以拟合到观察到的选择数据中,以及属性级别的影响可以有多准确地被估计出来。重要的结构问题包括每个选择集中的选项数量,以及同一选择集中的选项是否具有共同的属性级别。构建构成DCE的选择集集有两种广泛的方法——理论和算法——尽管对两种方法构建的设计进行了模拟研究和现场比较,但对于哪种方法能够始终提供更好的设计,并没有达成共识。
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