Instrumental Variable Analysis in Epidemiologic Studies: An Overview of the Estimation Methods

Abstract

Instrumental variables (IV)analysis seems an attractive method to control for unmeasured confounding in observational epidemiological studies. Here, we provide an overview of the estimation methods of IVanalysis and indicate their possible advantages and limitations.We found that two-stage least squares is the method of first choice if exposure and outcome are both continuous and show a linear relation. In case of a nonlinear relation, two-stage residual inclusion may be a suitable alternative. In settings with binary outcomes as well as nonlinear relations between exposure and outcome, generalized method of moments (GMM), structural mean models (SMM), and bivariate probit models perform well, yet GMM and SMM are generally more robust. The standard errors of the IVestimate can be estimated using a robust or bootstrap method. All estimation methods are prone to bias when the IVassumptions are violated. Researchers should be aware of the underlying assumptions of the estimation methods as well as the key assumptions of the IVwhen interpreting the exposure effects estimated through IV analysis.