对卫生保健的既定偏好进行匹配和加权

IF 2.8 3区 经济学 Q1 ECONOMICS
Caroline M. Vass , Marco Boeri , Christine Poulos , Alex J. Turner
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引用次数: 0

摘要

人们对使用陈述偏好方法来了解个人对健康和保健的偏好越来越感兴趣。人们对理解个体偏好的异质性也越来越感兴趣。因此,陈述偏好研究经常考虑捕获观察到的和未观察到的偏好异质性的模型。对异质性的一项流行的初步调查包括分样本分析,以比较亚组的偏好,例如,比较患者与临床医生,或比较老年患者与年轻患者。在固定效应模型中,恒定变量(个体特征)在选择集中保持稳定,因此只有在与各种属性和/或水平相互作用时才进入选择模型。然而,被调查者的子组可能在多个变量上有所不同,由于复杂性和缺乏权力,这些变量可能不容易用交互条款实现,因此在每个子组模型中通常只考虑一个或几个变量。本文概述了在亚组分析中匹配和平衡样本以对具有不同特征的个体进行加权的方法,并举例说明了未加权比较如何可能产生关于偏好异质性程度的错误结论。我们用合成和经验数据集来说明这个问题,以探索在简单选择模型之前和内部匹配子组的方法。结果表明,当异质性由多个因素驱动时,熵平衡和倾向得分匹配比使用不匹配偏好数据的分析更合适。本文最后讨论了匹配和加权在医疗保健决策中是否有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Matching and weighting in stated preferences for health care

There is an increasing interest in the use of stated preference methods to understand individuals' preferences for health and healthcare. There is also a growing interest in understanding heterogeneity in individuals' preferences. Consequently, stated preference studies frequently consider models that capture either or both observed and unobserved preference heterogeneity. A popular preliminary investigation into heterogeneity involves split-sample analysis to compare subgroups' preferences e.g., comparing patients with clinicians, or older patients with younger. In fixed-effects models, the constant variables (the individuals’ characteristics) remain stable across choice sets and therefore only enter the choice model when interacted with various attributes and/or levels. However, subgroups of respondents may differ on multiple variables that may not easily be implemented with interaction terms because of complexity and a lack of power thus only one, or a few, variables are typically taken into account in each subgroup model. This paper presents an overview of methods for matching and balancing samples to weight individuals with different characteristics in subgroup analysis and an example of how unweighted comparisons may produce erroneous conclusions regarding the degree of heterogeneity in preferences. We illustrate the issue with synthetic and empirical datasets to explore methods for matching subgroups before and within simple choice models. Our results show that entropy balancing and propensity score matching could be more appropriate than analyses using unmatched preference data when heterogeneity is driven by multiple factors. The paper concludes with a discussion of when matching and weighting may and may not be useful in healthcare decision-making.

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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
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