Statistical Modelling 9 (2009), 99–118
On the estimation of the misclassification table for finite count data with an
application in caries research
Emmanuel Lesaffre
Department of Biostatistics,
Erasmus Medical Centre,
Rotterdam
The Netherlands
and
L-Biostat,
Catholic University Leuven,
Leuven
Belgium
eMail:
emmanuel.lesaffre@med.kuleuven.be
Helmuth Küchenhoff
Department of Statistics,
Ludwig-Maximilians-Universität,
München
Germany
Samuel M Mwalili
Statistics and Actuarial Science,
Jomo Kenyatta University of Agriculture and Technology
Kenya
Dominique Declerck
School of Dentistry,
Catholic University of Leuven
Belgium
Abstract:
We look at the correction for misclassification of possibly corrupted finite
count data in epidemiological studies. In general, the misclassification
probabilities are estimated from a validation study and used to correct for
the distortion. However, most often the validation study is quite small
implying that the misclassification probabilities are impossible to calculate
or estimate with high variability if based on the multinomial distribution.
To increase efficiency, we propose an approach based on 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 which might mimic better the scoring behaviour of the
examiner relative to a gold standard. We evaluate the performance of our
approach(es) to estimate the misclassification probabilities in comparison
to the multinomial approach in an analytical way and in a simulation study.
Finally, the practical use of our methods is exemplified on an oral health
survey examining caries experience in 7-year-old Flemish children involving
16 dental examiners.
Keywords:
Count data; logistic regression; misclassification; prevalence; response error
back