A method to automate probabilistic sensitivity analyses of misclassified binary variables.
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.


CONCLUSIONS
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.
Fox
Matthew P
MP
Department of International Health, Boston University School of Public Health, Boston, MA, USA. mfox@bu.edu
Lash
Timothy L
TL
Greenland
Sander
S
D015982:Bias; D004812:Epidemiologic Methods; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology / Q000209:etiology; D009784:Occupational Diseases / Q000453:epidemiology / Q000209:etiology; D016273:Occupational Exposure / Q000009:adverse effects; D011237:Predictive Value of Tests; D012117:Resins, Synthetic / Q000009:adverse effects; D012680:Sensitivity and Specificity
D012117:Resins, Synthetic