Effects of Adjusting for Instrumental Variables on Bias and Precision of Effect Estimates

Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02120, USA.
American journal of epidemiology (Impact Factor: 5.23). 12/2011; 174(11):1213-22. DOI: 10.1093/aje/kwr364
Source: PubMed


Recent theoretical studies have shown that conditioning on an instrumental variable (IV), a variable that is associated with exposure but not associated with outcome except through exposure, can increase both bias and variance of exposure effect estimates. Although these findings have obvious implications in cases of known IVs, their meaning remains unclear in the more common scenario where investigators are uncertain whether a measured covariate meets the criteria for an IV or rather a confounder. The authors present results from two simulation studies designed to provide insight into the problem of conditioning on potential IVs in routine epidemiologic practice. The simulations explored the effects of conditioning on IVs, near-IVs (predictors of exposure that are weakly associated with outcome), and confounders on the bias and variance of a binary exposure effect estimate. The results indicate that effect estimates which are conditional on a perfect IV or near-IV may have larger bias and variance than the unconditional estimate. However, in most scenarios considered, the increases in error due to conditioning were small compared with the total estimation error. In these cases, minimizing unmeasured confounding should be the priority when selecting variables for adjustment, even at the risk of conditioning on IVs.

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    • "Methodological development in the application of instrumental variables and propensity score approaches is an area of active ongoing research (Austin, 2013; McCaffrey et al., 2013; Myers et al., 2011). As with any statistical technique, these methods can introduce bias if implemented incorrectly or if the instrumental variables are not well selected (Austin, 2008; Myers et al., 2011). Thus, investigators are encouraged to consult the most recent statistical literature when applying these methods. "
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    • "Moreover, when the outcome of interest is not rare and the number of potential confounders is moderate, they tend to provide results often indistinguishable from those provided by traditional multivariate techniques [20]. The results may be even more biased than those provided by other methods when, in presence of relatively strong unmeasured confounders, variables associated with the exposure but not associated with the outcome are erroneously included in the model used to estimate the PS [21,22]. "
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    • "The researcher who eliminates some unmeasured confounders through the use of an instrumental variable such as physician identity may also remove major sources of pseudorandom variation through the same maneuver [5]. If unmeasured confounders remain after matching on an instrument , they may be the only remaining determinants of variation in treatment within the matched group. "
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