Department of Public Health Sciences, The University of Chicago, Chicago, Illinois.
Department of Medicine, The University of Chicago, Chicago, Illinois.
Health Econ. 2019 May;28(5):710-716. doi: 10.1002/hec.3861. Epub 2019 Jan 22.
Health economists are often interested in the effects of provider-level attributes (e.g., nonprofit status or quality rating) on patient outcomes, but estimation is subject to selection bias due to correlation with other omitted provider-level attributes that also affect patient outcomes. Recently, researchers have attempted to use patient-level instrumental variables, such as differential distance, to solve this problem of a provider-level endogenous treatment variable in settings where patients are nested within providers. However, to satisfy validity assumptions, an instrumental variable for a provider attribute must be at the provider level or a larger unit of aggregation, not at the patient level. A patient-level instrument cannot predict variation in a provider attribute separately from other, potentially unmeasured, provider attributes. In this paper, we explain this misapplication, review the extent of this problem in recent literature, and offer alternative approaches to avoid this misapplication of patient-level instrumental variables.
卫生经济学家通常关注提供者层面的属性(如非营利性或质量评级)对患者结果的影响,但由于与其他可能影响患者结果的提供者层面属性相关,因此估计会受到选择偏差的影响。最近,研究人员试图使用患者层面的工具变量,如差异距离,来解决在患者嵌套在提供者中的情况下,提供者层面内生治疗变量的问题。然而,为了满足有效性假设,提供者属性的工具变量必须在提供者层面或更大的聚合层面上,而不是在患者层面上。患者层面的工具变量不能单独预测提供者属性的变化,而其他潜在未测量的提供者属性可能会影响提供者属性的变化。在本文中,我们解释了这种错误应用,回顾了最近文献中该问题的程度,并提供了避免这种患者层面工具变量误用的替代方法。