MethodsImplementation of Instrumental Variable Bounds for Data Missing Not at RandomMarden, Jessica R.a; Wang, Linbob; Tchetgen, Eric J. Tchetgena,b; Walter, Stefanc; Glymour, M. Mariac; Wirth, Kathleen E.a,dAuthor Information From the aDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA; bDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA; cDepartment of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA; and dDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston MA. Submitted July 14, 2016; accepted January 29, 2018. Supported by the National Institute for Allergy and Infectious Disease (R37 AI 51164, R21 AI 113251, and R01 AI 104459). The authors report no conflicts of interest. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Correspondence: Kathleen E. Wirth, Departments of Epidemiology and Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115. E-mail: [email protected]. Epidemiology: May 2018 - Volume 29 - Issue 3 - p 364-368 doi: 10.1097/EDE.0000000000000811 Buy SDC Metrics Abstract Instrumental variables are routinely used to recover a consistent estimator of an exposure causal effect in the presence of unmeasured confounding. Instrumental variable approaches to account for nonignorable missing data also exist but are less familiar to epidemiologists. Like instrumental variables for exposure causal effects, instrumental variables for missing data rely on exclusion restriction and instrumental variable relevance assumptions. Yet these two conditions alone are insufficient for point identification. For estimation, researchers have invoked a third assumption, typically involving fairly restrictive parametric constraints. Inferences can be sensitive to these parametric assumptions, which are typically not empirically testable. The purpose of our article is to discuss another approach for leveraging a valid instrumental variable. Although the approach is insufficient for nonparametric identification, it can nonetheless provide informative inferences about the presence, direction, and magnitude of selection bias, without invoking a third untestable parametric assumption. An important contribution of this article is an Excel spreadsheet tool that can be used to obtain empirical evidence of selection bias and calculate bounds and corresponding Bayesian 95% credible intervals for a nonidentifiable population proportion. For illustrative purposes, we used the spreadsheet tool to analyze HIV prevalence data collected by the 2007 Zambia Demographic and Health Survey (DHS). Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.