A tutorial on the use of instrumental variables in pharmacoepidemiology. Issue 4 (27th February 2017)

• Record Type: Journal Article
• Authors: Ertefaie, Ashkan; Small, Dylan S.; Flory, James H.; Hennessy, Sean
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 26:Issue 4(2017)
• Page Start: 357
• Page End: 367
• Publication Date: 2017-02-27
• Subjects: assumptions -- instrumental variables -- observational studies -- sensitivity analysis -- unmeasured confounders -- pharmacoepidemiology; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4158
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: Instrumental variable (IV) methods are used increasingly in pharmacoepidemiology to address unmeasured confounding. In this tutorial, we review the steps used in IV analyses and the underlying assumptions. We also present methods to assess the validity of those assumptions and describe sensitivity analysis to examine the effects of possible violations of those assumptions. Methods: Observational studies based on regression or propensity score analyses rely on the untestable assumption that there are no unmeasured confounders. IV analysis is a tool that removes the bias caused by unmeasured confounding provided that key assumptions (some of which are also untestable) are met. Results: When instruments are valid, IV methods provided unbiased treatment effect estimation in the presence of unmeasured confounders. However, the standard error of the IV estimate is higher than the standard error of non‐IV estimates, e.g., regression and propensity score methods. Sensitivity analyses provided insight about the robustness of the IV results to the plausible degrees of violation of assumptions. Conclusions: IV analysis should be used cautiously because the validity of IV estimates relies on assumptions that are, in general, untestable and difficult to be certain about. Thus, assessing the sensitivity of the estimate to violations of these assumptions is important and can better inform the causal inferences that can be drawn from the study. Copyright © 2017 John Wiley & Sons, Ltd.