Bayesian Correction for Misclassification in Multilevel Count Data Models. (25th February 2018)

• Record Type: Journal Article
• Authors: Nelson, Tyler; Song, Joon Jin; Chin, Yoo-Mi; Stamey, James D.
• Other Names: Winkler David A. Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2018(2018)
• Publication Date: 2018-02-25
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2018/3212351
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.