A Bayesian approach for misclassified ordinal response data

ABSTRACT

Motivated by a longitudinal oral health study, the Signal-Tandmobiel^® study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulation-based example and the analysis of the motivating study.

KEYWORDS:

• Bayesian analysis
• data augmentation
• Markov chain Monte Carlo methods
• misclassification
• ordinal regression model

CLASSIFICATION CODES:

• 62F15
• 62J99
• 62P10

Acknowledgements

The Signal-Tandmobiel^® study comprises the following partners: D. Declerck (Dental School, Katholieke Universiteit Leuven), L. Martens (Dental School, University of Ghent), J. Vanobbergen (Dental School, University of Ghent), P. Bottenberg (Dental School, University of Brussels), E. Lesaffre (L-BioStat, Katholieke Universiteit Leuven), and K. Hoppenbrouwers (Youth Health Department, Katholieke Universiteit Leuven, and Flemish Association for Youth Health Care).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research has been supported by Agencia Estatal de Investigación, Spain (Projects MTM2014-56949-C3-3-R and MTM2017-86875-C3-2-R), Junta de Extremadura, Spain (Project GRU18108), and European Regional Development Union Funds. First author has also been supported by Sociedad Matemática Mexicana and Fundación Sofía Kovalévskaia.