American Journal of Epidemiology Advance Access originally published online on April 26, 2006
American Journal of Epidemiology 2006 164(1):63-68; doi:10.1093/aje/kwj155
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Original Contribution |
Accounting for Independent Nondifferential Misclassification Does Not Increase Certainty that an Observed Association Is in the Correct Direction
1 Department of Epidemiology, University of California, Los Angeles, CA
2 Department of Statistics, University of California, Los Angeles, CA
3 Department of Statistics, University of British Columbia, Vancouver, Canada
Correspondence to Dr. Sander Greenland, Departments of Epidemiology and Statistics, University of California, Los Angeles, CA 90095-1772 (e-mail: lesdomes{at}ucla.edu).
Researchers sometimes argue that their exposure-measurement errors are independent of other errors and are nondifferential with respect to disease, resulting in estimation bias toward the null. Among well-known problems with such arguments are that independence and nondifferentiality are harder to satisfy than ordinarily appreciated (e.g., because of correlation of errors in questionnaire items, and because of uncontrolled covariate effects on error rates); small violations of independence or nondifferentiality may lead to bias away from the null; and, if exposure is polytomous, the bias produced by independent nondifferential error is not always toward the null. The authors add to this list by showing that, in a 2 x 2 table (for which independent nondifferential error produces bias toward the null), accounting for independent nondifferential error does not reduce the p value even though it increases the point estimate. Thus, such accounting should not increase certainty that an association is present.
bias; epidemiologic methods; measurement error; misclassification; odds ratio; relative risk; validation
Abbreviations: IN, independent nondifferential; MLE, maximum likelihood estimate
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