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- W2420070117 abstract "Instrumental variable analysis is an increasingly popular statistical method in epidemiologic 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 Swanson3 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." @default.
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- W2420070117 date "2015-07-01" @default.
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- W2420070117 title "Commentary" @default.
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- W2420070117 doi "https://doi.org/10.1097/ede.0000000000000302" @default.
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