Date of this Version
Statistics in Medicine
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.
This is the peer reviewed version of the following article: Guo Z., Cheng J., Lorch S. A., and Small D. S. (2014), Using an Instrumental Variable to Test for Unmeasured Confounding, Statist. Med., 33; pages 3528–3546., which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.6227/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms.
instrumental variables, observational study, confounding, comparative effectiveness
Cheng, J., Lorch, S. A., Small, D. S., & Guo, Z. (2014). Using an Instrumental Variable to Test for Unmeasured Confounding. Statistics in Medicine, 33 (20), 3528-3546. http://dx.doi.org/10.1002/sim.6227
Date Posted: 27 November 2017
This document has been peer reviewed.