Commentary: An Even Clearer Portrait of Bias in Observational Studies?

Commentary I nstrumental variable analysis is an increasingly popular statistical method in epidemio-logic research. 1 epidemiologists' enthusiasm for this approach may be because it can potentially estimate causal effects in observational data in the presence of unmeasured confounding. 2 This overcomes a significant limitation of conventional epidemiologic methods, such as multivariable regression analysis: residual confounding. However, it is also possible for instrumental variable analyses to suffer from residual confounding. this can occur if the proposed instruments are associated with unmeasured confounding factors. therefore , the key question for empirical researchers, regulators, and clinicians is: which is more biased—conventional multivariable adjusted regression or instrumental variable analysis? In this issue of Epidemiology, Jackson and Swanson 3 elegantly describe a method for presenting and comparing the balance of potential confounders across values of the instrument and the actual treatment. this can allow researchers to assess the relative bias that could be caused by observed confounding factors. these methods may provide information about the relative bias of the unobserved confounders if they are correlated with the observed confounders. I will briefly discuss the methodologic improvements proposed by this article, its limitations, and finally a potential solution to these limitations. the core of the paper is illustrated by a standard linear model: Y x x U x () = + + + α α α 0 1 2 ε , where Y, x, U, and z, respectively, represent a binary outcome, the treatment, a confounder of the outcome-treatment relationship, and the instrument. ϵ x represents the error term, α 0 is a constant, α 1 is the effects of the treatment, and α 2 is the effects of the confounder on the outcome. the simplest approach for assessing bias is to compare the difference in each con-founder across values of the actual treatment, x, to the difference in the confounder across values of the instrument z. However, the same difference in a confounder across values of the instrument will result in much larger bias in the instrumental variable estimator than for the ordinary least squares (OLS) estimator. To overcome this, Brookhart and Schneeweiss 4 proposed reporting the relative bias of the OLS and instrumental variable estimators of α 1 when the confounder U is omitted. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is …