The authors examine some recently proposed criteria for determining when to adjust for covariates related to mlsdassification, and show these criteria to be incorrect In particular, they show that when misdassffication 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 misclasstflcation 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 mis-classification is present These results demonstrate that it is necessary to consider the degree of misdassiflcation when deciding whether to control a covariate. (Author/publisher)
Abstract