Using an instrumental variable to test for unmeasured confounding

被引:25
作者
Guo, Zijian [1 ]
Cheng, Jing [2 ]
Lorch, Scott A. [3 ]
Small, Dylan S. [1 ]
机构
[1] Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA
[2] Univ Calif San Francisco, Sch Dent, Div Oral Epidemiol & Dent Publ Hlth, San Francisco, CA 94143 USA
[3] Univ Penn, Dept Pediat, Philadelphia, PA 19104 USA
基金
美国国家科学基金会;
关键词
instrumental variables; observational study; confounding; comparative effectiveness; TO-TREAT ANALYSIS; CAUSAL INFERENCE; TREATMENT-NONCOMPLIANCE; ACCELERATION; DELIVERY; OUTCOMES; MODELS; CARE;
D O I
10.1002/sim.6227
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
An important concern in an observational study is whether or not there is unmeasured confounding, that is, unmeasured ways in which the treatment and control groups differ before treatment, which affect the outcome. We develop a test of whether there is unmeasured confounding when an instrumental variable (IV) is available. An IV is a variable that is independent of the unmeasured confounding and encourages a subject to take one treatment level versus another, while having no effect on the outcome beyond its encouragement of a certain treatment level. We show what types of unmeasured confounding can be tested for with an IV and develop a test for this type of unmeasured confounding that has correct type I error rate. We show that the widely used Durbin-Wu-Hausman test can have inflated type I error rates when there is treatment effect heterogeneity. Additionally, we show that our test provides more insight into the nature of the unmeasured confounding than the Durbin-Wu-Hausman test. We apply our test to an observational study of the effect of a premature infant being delivered in a high-level neonatal intensive care unit (one with mechanical assisted ventilation and high volume) versus a lower level unit, using the excess travel time a mother lives from the nearest high-level unit to the nearest lower-level unit as an IV. Copyright (C) 2014 John Wiley & Sons, Ltd.
引用
收藏
页码:3528 / 3546
页数:19
相关论文
共 49 条
[1]   Bootstrap tests for distributional treatment effects in instrumental variable models [J].
Abadie, A .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2002, 97 (457) :284-292
[2]  
Adkins LC, ADV ECONOMETRICS, V29, P515
[3]  
Angrist J., 1999, Handb. Labor Econ., V3, P1277, DOI [10.1016/S1573-4463(99)03004-7, DOI 10.1016/S1573-4463(99)03004-7]
[4]  
Angrist JD, 1996, J AM STAT ASSOC, V91, P444, DOI 10.2307/2291629
[5]  
[Anonymous], 2010, Econometric analysis of cross section and panel data
[6]  
[Anonymous], 2007, Numerical Recipes
[7]   Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants [J].
Baiocchi, Mike ;
Small, Dylan S. ;
Lorch, Scott ;
Rosenbaum, Paul R. .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2010, 105 (492) :1285-1296
[8]   Use of instrumental variables in the presence of heterogeneity and self-selection: An application to treatments of breast cancer patients [J].
Basu, Anirban ;
Heckman, James J. ;
Navarro-Lozano, Salvador ;
Urzua, Sergio .
HEALTH ECONOMICS, 2007, 16 (11) :1133-1157
[9]  
Bowden R., 1984, Instrumental variables
[10]   Instrumental variable methods in comparative safety and effectiveness research [J].
Brookhart, M. Alan ;
Rassen, Jeremy A. ;
Schneeweiss, Sebastian .
PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2010, 19 (06) :537-554