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The zero-inflated negative binomial regression model with correction for misclassification: an example in caries researchStatistics and Actuarial Sciences, Jorno Kenyatta University of Agriculture and Technology, Kenya
Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands, Biostatistical Centre, Katholieke Universiteit, Leuven, Belgium, Emmanuel.Lesaffre{at}med.kuleuven.be
School of Dentistry, Katholieke Universiteit, Leuven, Belgium 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 binomial model and the Poisson model. Here we look at a more complex model, that is, the zero-inflated negative binomial, and illustrate how correction for misclassification can be achieved. Our approach is illustrated on the dmft-index which is a popular measure for caries experience in caries research. An extra problem was the fact that several dental examiners were involved in scoring caries experience. Using our example, we illustrate how a non-differential misclassification process for each examiner can lead to differential misclassification overall.
This version was published on April
1, 2008 Statistical Methods in Medical Research, Vol. 17, No. 2,
123-139 (2008) This article has been cited by other articles:
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