BACKGROUND Confounding of mediator-outcome associations resulting in collider biases causes systematic error when estimating direct and indirect effects. However, until recently little attention has been given to the impact of misclassification bias. OBJECTIVE To quantify the impact of non-differential and independent misclassification of a dichotomous exposure and a dichotomous mediator on t...

AIM To estimate the change in years of life lost (YLL) due to gastric cancer mortality after correcting for misclassification in registering causes of death using the Bayesian method. BACKGROUND For evaluating the health status of a country and determining priority of risk factors, some epidemiologic indicators are needed. Due to premature death, YLL is one of the most widely used indicators....

OBJECTIVES Demonstration of the applicability of a framework called indirect classification to the example of glaucoma classification. Indirect classification combines medical a priori knowledge and statistical classification methods. The method is compared to direct classification approaches with respect to the estimated misclassification error. METHODS Indirect classification is applied usi...

Zero-inflated models for count data are becoming quite popular nowadays and are found in many application areas, such as medicine, economics, biology, sociology and so on. However, in practice these counts are often prone to measurement error which in this case boils down to misclassification. Methods to deal with misclassification of counts have been suggested recently, but only for the binomi...

It is now recognized that exposure to environmental tobacco smoke (ETS) in the workplace and other settings outside the home may be equally as important as residential ETS exposure. This review examines the sources of misclassification in the assessment of workplace ETS exposure in questionnaire-based epidemiologic studies. Cogent to this discussion is the role of misclassification of ever smok...

We discuss alternative approaches for estimating from cross-sectional categorical data in the presence of misclassification. Two parameterisations of the misclassification model are reviewed. The first employs misclassification probabilities and leads tomoment-based inference. The second employs calibration probabilities and leads tomaximum likelihood inference. We show that maximum likelihood ...