On the estimation of the misclassification table for finite count data with an application in caries research

摘要

We look at the correction for misclassification of possibly corrupted finite count data inepidemiological studies. In general, the misclassification probabilities are estimated from a validationstudy and used to correct for the distortion. However, most often the validation study is quite smallimplying that the misclassification probabilities are impossible to calculate or estimate with high vari-ability if based on the multinomial distribution. To increase efficiency, we propose an approach basedon the fact that to determine a count the examiner needs to evaluate all items that make up that count,called the double binomial (DB) approach. We suggest various extensions of the DB approach whichmight mimic better the scoring behaviour of the examiner relative to a gold standard. We evaluatethe performance of our approach(es) to estimate the misclassification probabilities in comparison tothe multinomial approach in an analytical way and in a simulation study. Finally, the practical use ofour methods is exemplified on an oral health survey examining caries experience in 7-year-old Flemishchildren involving 16 dental examiners.