IJE Advance Access originally published online on September 19, 2005
International Journal of Epidemiology 2005 34(6):1370-1376; doi:10.1093/ije/dyi184
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A method to automate probabilistic sensitivity analyses of misclassified binary variables
1 Department of International Health, Boston University School of Public Health, Boston, MA, USA.
2 Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
3 Geriatrics Section, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
4 Departments of Epidemiology and Statistics, University of California, Los Angeles, CA, USA.
* Corresponding author. E-mail: mfox{at}bu.edu
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
Accepted 9 August 2005
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