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IJE Advance Access published online on September 19, 2005
International Journal of Epidemiology, doi:10.1093/ije/dyi184
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2005; all rights reserved.
Accepted August 9, 2005

Original paper

A method to automate probabilistic sensitivity analyses of misclassified binary variables

Matthew P. Fox 1*, Timothy L. Lash 2, and Sander Greenland 3

1 Department of International Health, Boston University School of Public Health, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
2 Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA; Geriatrics Section, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
3 Department of Epidemiology, University of California, Los Angeles, CA, USA; Department of Statistics, University of California, Los Angeles, CA, USA

* To whom correspondence should be addressed.
Matthew P. Fox, E-mail: mfox{at}bu.edu


  Abstract

Background Misclassification bias is present in most studies, yet uncertainty about its magnitude or direction is rarely quantified.

Methods The authors present a method for probabilistic sensitivity analysis to quantify likely effects of misclassification of a dichotomous outcome, exposure or covariate. This method involves reconstructing the data that would have been observed had the misclassified variable been correctly classified, given the sensitivity and specificity of classification. The accompanying SAS macro implements the method and allows users to specify ranges of sensitivity and specificity of misclassification parameters to yield simulation intervals that incorporate both systematic and random error.

Results The authors illustrate the method and the accompanying SAS macro code by applying it to a study of the relation between occupational resin exposure and lung-cancer deaths. The authors compare the results using this method with the conventional result, which accounts for random error only, and with the original sensitivity analysis results.

Conclusion By accounting for plausible degrees of misclassification, investigators can present study results in a way that incorporates uncertainty about the bias due to misclassification, and so avoid misleadingly precise-looking results.

Keywords: Epidemiological methods; misclassification; Monte Carlo method; sensitivity and specificity; sensitivity analysis.
A Commentary has been commissioned to accompany this article and will appear with this paper in the printed issue.
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