title: Confounding and misclassification.
abstract: The authors examine some recently proposed criteria for determining when to adjust for covariates related to misclassification, and show these criteria to be incorrect. In particular, they show that when misclassification is present, covariate control can sometimes increase net bias, even when the covariate would have been a confounder under perfect classification, and even if the covariate is a determinant of classification. Thus, bias due to misclassification cannot be adequately dealt with by the methods used for control of confounding. The examples presented also show that the "change-in-estimate" criterion for deciding whether to control a covariate can be systematically misleading when misclassification is present. These results demonstrate that it is necessary to consider the degree of misclassification when deciding whether to control a covariate.
mesh_terms: D001699:Biometry; D003951:Diagnostic Errors; D004812:Epidemiologic Methods; D006801:Humans; D012306:Risk; D013223:Statistics as Topic