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American Journal of Epidemiology Advance Access published online on July 5, 2007
American Journal of Epidemiology, doi:10.1093/aje/kwm165
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American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

The Impact of Residual and Unmeasured Confounding in Epidemiologic Studies: A Simulation Study

Zoe Fewell, George Davey Smith and Jonathan A. C. Sterne

From the Department of Social Medicine, University of Bristol, Bristol, United Kingdom

Correspondence to Prof. Jonathan A. C. Sterne, Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Road, Bristol BS8 2PR, United Kingdom (e-mail: Jonathan.Sterne{at}bristol.ac.uk).

Received for publication April 11, 2005. Accepted for publication November 11, 2005.

Measurement error in explanatory variables and unmeasured confounders can cause considerable problems in epidemiologic studies. It is well recognized that under certain conditions, nondifferential measurement error in the exposure variable produces bias towards the null. Measurement error in confounders will lead to residual confounding, but this is not a straightforward issue, and it is not clear in which direction the bias will point. Unmeasured confounders further complicate matters. There has been discussion about the amount of bias in exposure effect estimates that can plausibly occur due to residual or unmeasured confounding. In this paper, the authors use simulation studies and logistic regression analyses to investigate the size of the apparent exposure-outcome association that can occur when in truth the exposure has no causal effect on the outcome. The authors consider two cases with a normally distributed exposure and either two or four normally distributed confounders. When the confounders are uncorrelated, bias in the exposure effect estimate increases as the amount of residual and unmeasured confounding increases. Patterns are more complex for correlated confounders. With plausible assumptions, effect sizes of the magnitude frequently reported in observational epidemiologic studies can be generated by residual and/or unmeasured confounding alone.

bias (epidemiology); computer simulation; confounding factors (epidemiology); logistic models

Abbreviations: ICC, intraclass correlation coefficient

Editor's note: An invited commentary on this article appears on page 000, and the authors' response appears on page 000.


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