Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence.
Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence.
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DOI：
10.1002/hec.2927
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发表时间：
2013-09
期刊：
影响因子：
2.1
通讯作者：
Basu, Anirban
中科院分区：
文献类型：
--
作者：
Borah, Bijan J.;Basu, Anirban
关键词：
分位数回归（QR）框架提供了一种实用的方法来理解沿结果分布的协变量的差异影响。然而，应用经济学文献中普遍存在的QR框架是基于条件分位数回归方法的。它用于评估协变量对结果的分位数的影响，条件是其他协变量的特定值。在大多数情况下，条件分位数回归可能生成的结果在政策或人口上下文中通常不可概括或解释。相比之下，无条件分位数回归方法提供了更多可解释的结果，因为它边缘化了对模型中其他协变量分布的影响。在本文中，强调了这两种回归框架之间的差异，无论是概念上还是计量上。此外，利用来自美国一家大型健康保险公司的真实索赔数据，实施了替代QR框架，以评估老年阿尔茨海默病患者服药依从性分布中协变量的差异影响。
The quantile regression (QR) framework provides a pragmatic approach in understanding the differential impacts of covariates along the distribution of an outcome. However, the QR framework that has pervaded the applied economics literature is based on the conditional quantile regression method. It is used to assess the impact of a covariate on a quantile of the outcome conditional on specific values of other covariates. In most cases, conditional quantile regression may generate results that are often not generalizable or interpretable in a policy or population context. In contrast, the unconditional quantile regression method provides more interpretable results as it marginalizes the effect over the distributions of other covariates in the model. In this paper, the differences between these two regression frameworks are highlighted, both conceptually and econometrically. Additionally, using real-world claims data from a large US health insurer, alternative QR frameworks are implemented to assess the differential impacts of covariates along the distribution of medication adherence among elderly patients with Alzheimer’s disease.
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