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Correcting for misclassification for a monotone disease process with an application in dental research
Author(s) -
GarcíaZattera M. J.,
Mutsvari T.,
Jara A.,
Declerck D.,
Lesaffre E.
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3906
Subject(s) - covariate , monotone polygon , computer science , estimator , bayesian probability , simple (philosophy) , extension (predicate logic) , statistics , markov model , econometrics , markov chain , mathematics , machine learning , artificial intelligence , philosophy , geometry , epistemology , programming language
Motivated by a longitudinal oral health study, we evaluate the performance of binary Markov models in which the response variable is subject to an unconstrained misclassification process and follows a monotone or progressive behavior. Theoretical and empirical arguments show that the simple version of the model can be used to estimate the prevalence, incidences, and misclassification parameters without the need of external information and that the incidence estimators associated with the model outperformed approaches previously proposed in the literature. We propose an extension of the simple version of the binary Markov model to describe the relationship between the covariates and the prevalence and incidence allowing for different classifiers. We implemented a Bayesian version of the extended model and show that, under the settings of our motivating example, the parameters can be estimated without any external information. Finally, the analyses of the motivating problem are presented. Copyright © 2010 John Wiley & Sons, Ltd.