Understanding the Assumptions Underlying Instrumental Variable Analyses: a Brief Review of Falsification Strategies and Related Tools

• Published in: Current epidemiology reports Vol. 5; no. 3; pp. 214 - 220
• Main Authors: Labrecque, Jeremy, Swanson, Sonja A.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.01.2018; Springer Nature B.V
• Subjects: Epidemiologic Methods (R Maclehose; Epidemiologic Methods (R Maclehose, Section Editor); Epidemiology; Medicine; Medicine & Public Health; Section Editor; Topical Collection on Epidemiologic Methods; Instrumental variable; Falsification; Mendelian randomization
• ISSN: 2196-2995; 2196-2995
• DOI: 10.1007/s40471-018-0152-1

Purpose of Review Instrumental variable (IV) methods continue to be applied to questions ranging from genetic to social epidemiology. In the epidemiologic literature, discussion of whether the assumptions underlying IV analyses hold is often limited to only certain assumptions and even then, arguments are mostly made using subject matter knowledge. To complement subject matter knowledge, there exist a variety of falsification strategies and other tools for weighing the plausibility of the assumptions underlying IV analyses. Recent Findings There are many tools that can refute the IV assumptions or help estimate the magnitude or direction of possible bias if the conditions do not hold perfectly. Many of these tools, including both recently developed strategies and strategies described decades ago, are underused or only used in specific applications of IV methods in epidemiology. Summary Although estimating causal effects with IV analyses relies on unverifiable assumptions, the assumptions can sometimes be refuted. We suggest that the epidemiologists using IV analyses employ all the falsification strategies that apply to their research question in order to avoid settings that demonstrably violate a core condition for valid inference.