Near/far matching: A study design approach to instrumental variables

被引：41

作者：

Mike Baiocchi

Dylan S. Small

Lin Yang

Daniel Polsky

Peter W. Groeneveld

机构：

[1] Department of Statistics, Stanford University, 390 Serra Mall

[2] Department of Veterans Affairs, Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia PA

[3] Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia PA

[4] Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia PA

[5] Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia PA

来源：

Health Services and Outcomes Research Methodology | 2012年 / 12卷 / 4期

基金：

美国国家科学基金会;

关键词：

Binary outcomes; Comparative effectiveness; Instrumental variables; Matching; Medicare data; Study design;

D O I：

10.1007/s10742-012-0091-0

中图分类号：

学科分类号：

摘要：

Classic instrumental variable techniques involve the use of structural equation modeling or other forms of parameterized modeling. In this paper we use a nonparametric, matching-based instrumental variable methodology that is based on a study design approach. Similar to propensity score matching, though unlike classic instrumental variable approaches, near/far matching is capable of estimating causal effects when the outcome is not continuous. Unlike propensity score matching, though similar to instrumental variable techniques, near/far matching is also capable of estimating causal effects even when unmeasured covariates produce selection bias. We illustrate near/far matching by using Medicare data to compare the effectiveness of carotid arterial stents with cerebral protection versus carotid endarterectomy for the treatment of carotid stenosis. © 2012 Springer Science+Business Media, LLC.

引用

页码：237 / 253

页数：16

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