Bayesian adjustment for unidirectional misclassification in ordinal covariates

ABSTRACT

In this paper, we study the identification of Bayesian regression models, when an ordinal covariate is subject to unidirectional misclassification. Xia and Gustafson [Bayesian regression models adjusting for unidirectional covariate misclassification. Can J Stat. 2016;44(2):198–218] obtained model identifiability for non-binary regression models, when there is a binary covariate subject to unidirectional misclassification. In the current paper, we establish the moment identifiability of regression models for misclassified ordinal covariates with more than two categories, based on forms of observable moments. Computational studies are conducted that confirm the theoretical results. We apply the method to two datasets, one from the Medical Expenditure Panel Survey (MEPS), and the other from Translational Research Investigating Underlying Disparities in Acute Myocardial infarction Patients Health Status (TRIUMPH).

KEYWORDS:

• Bayesian inference
• Covariate misclassification
• Unidirectional misclassification
• Markov Chain Monte Carlo

AMS SUBJECT CLASSIFICATION:

• 62F15 (Bayesian inference)

Acknowledgements

The authors are extremely grateful to the editor, associate editor and anonymous referees for their valuable comments and suggestions that significantly improved the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The TRIUMPH study was funded by a grant from the National Heart, Lung and Blood Institute [P50 HL 077113].