title: A tutorial on the use of instrumental variables in pharmacoepidemiology.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.|Department of statistics, University of Pennsylvania, Philadelphia, PA, USA.|Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Ashkan
authors.initials: A
authors.lastname: Ertefaie
authors.affiliation: Department of statistics, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Dylan S
authors.initials: DS
authors.lastname: Small
authors.affiliation: Weill-Cornell School of Medicine, New York, USA.
authors.forename: James H
authors.initials: JH
authors.lastname: Flory
authors.affiliation: Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Sean
authors.initials: S
authors.lastname: Hennessy
mesh_terms: D015982:Bias; D015986:Confounding Factors, Epidemiologic; D015340:Epidemiologic Research Design; D006801:Humans; D017891:Pharmacoepidemiology / Q000379:methods*; D057216:Propensity Score
keywords: assumptions; instrumental variables; observational studies; pharmacoepidemiology; sensitivity analysis; unmeasured confounders