| Titre : | A method to automate probabilistic sensitivity analyses of misclassified binary variables. Commentary. (2005) |
| Auteurs : | Matthew-P FOX ; GREENLAND (Sander) : USA. Departments of Epidemiology and Statistics. University of California. Los Angeles. CA. ; LASH (Timothy-L) : USA. Geriatrics Section. Department of Medicine. Boston University School of Medicine. Boston. MA. ; MARSHALL (James-R) / disc. : USA. Cancer Prevention and Population Sciences. Roswell Park Cancer Institute. Elm and Carlton Streets. Buffalo. NY. ; Boston University School of Public Health. Department of Epidemiology. Boston. MA. USA |
| Type de document : | Article |
| Dans : | International journal of epidemiology (vol. 34, n° 6, 2005) |
| Pagination : | 1370-1377 |
| Langues: | Anglais |
| Mots-clés : | Classification ; Epidémiologie ; Méthode ; Sensibilité |
| Résumé : | [BDSP. Notice produite par INIST-CNRS R0xe5JE4. Diffusion soumise à autorisation]. 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. |
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