Bayesian adjustment for unidirectional misclassification in ordinal covariates |
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| Authors: | Liangrui Sun Michelle Xia Yuanyuan Tang Philip G. Jones |
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| Affiliation: | 1. Department of Statistics, University of Nebraska–Lincoln, Lincoln, NE, USA;2. Division of Statistics, Northern Illinois University, Dekalb, IL, USA;3. Saint Luke's Mid America Heart Institute, Saint Luke's Hospital of Kansas City, Saint Luke's Health System, Kansas City, MO, USA |
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| 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). |
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| Keywords: | Bayesian inference Covariate misclassification Unidirectional misclassification Markov Chain Monte Carlo |
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