Statistical Methods in Medical Research

 

Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here for more information

Sign In to gain access to subscriptions and/or personal tools.
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
0962280206071840v1
17/2/123    most recent
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrow Add to My Marked Citations
Google Scholar
Right arrow Articles by Mwalili, S. M
Right arrow Articles by Declerck, D.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Mwalili, S. M
Right arrow Articles by Declerck, D.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?
This version was published on April 1, 2008
Statistical Methods in Medical Research, Vol. 17, No. 2, 123-139 (2008)
DOI: 10.1177/0962280206071840
© 2008 SAGE Publications

The zero-inflated negative binomial regression model with correction for misclassification: an example in caries research

Samuel M Mwalili

Statistics and Actuarial Sciences, Jorno Kenyatta University of Agriculture and Technology, Kenya

Emmanuel Lesaffre

Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands, Biostatistical Centre, Katholieke Universiteit, Leuven, Belgium, Emmanuel.Lesaffre{at}med.kuleuven.be

Dominique Declerck

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.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?