An underappreciated misclassification mechanism: implications of nondifferential dependent misclassification of covariate and exposure

Abstract

Misclassification is a pervasive problem in assessing relations between exposures and outcomes. While some attention has been paid to the impact of dependence in measurement error between exposures and outcomes, there is little awareness of the potential impact of dependent error between exposures and covariates, despite the fact that this latter dependency may occur much more frequently, for example, when both are assessed by questionnaire. We explored the impact of nondifferential dependent exposure-confounder misclassification bias by simulating a dichotomous exposure (E), disease (D) and covariate (C) with varying degrees of non-differential dependent misclassification between C and E. We demonstrate that under plausible scenarios, an adjusted association can be a poorer estimate of the true association than the crude. Correlated errors in the measurement of covariate and exposure distort the covariate-exposure, covariate-outcome and exposure-outcome associations creating observed associations that can be greater than, less than, or in the opposite direction of the true associations. Under these circumstances adjusted associations may not be bounded by the crude association and true effect, as would be expected with nondifferential independent confounder misclassification. The degree and direction of distortion depends on the amount of dependent error, prevalence of covariate and exposure, and magnitude of true effect.