Instrumental variables (IV) are used to draw causal conclusions about the effect of exposure E on outcome Y in the presence of unmeasured confounders. IV assumptions have been well described: (1) IV affects E; (2) IV affects Y only through E; (3) IV shares no common cause with Y. Even when these assumptions are met, biased effect estimates can result if selection bias allows a noncausal path from E to Y. We demonstrate the presence of bias in IV analyses on a sample from a simulated dataset, where selection into the sample was a collider on a noncausal path from E to Y. By applying inverse probability of selection weights, we were able to eliminate the selection bias. IV approaches may protect against unmeasured confounding but are not immune from selection bias. Inverse probability of selection weights used with IV approaches can minimize bias.
From the Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Submitted 16 May 2016; accepted 31 January 2017.
Supported by NIH Grants U01-HL121812, U24-AA020801, and T32-AI102623.
The authors report no conflicts of interest.
All data reported in this analysis are simulated. Code to replicate the simulation is provided in the eAppendix (http://links.lww.com/EDE/B174).
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
Correspondence: Chelsea Canan, 615 N. Wolfe St., Baltimore, MD 21205. E-mail: firstname.lastname@example.org.