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Bayesian Correction for Misclassification in Multilevel Count Data Models

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...

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Detalles Bibliográficos
Autores principales: Nelson, Tyler, Song, Joon Jin, Chin, Yoo-Mi, Stamey, James D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5845492/
https://www.ncbi.nlm.nih.gov/pubmed/29681994
http://dx.doi.org/10.1155/2018/3212351
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author Nelson, Tyler
Song, Joon Jin
Chin, Yoo-Mi
Stamey, James D.
author_facet Nelson, Tyler
Song, Joon Jin
Chin, Yoo-Mi
Stamey, James D.
author_sort Nelson, Tyler
collection PubMed
description 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.
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spelling pubmed-58454922018-04-21 Bayesian Correction for Misclassification in Multilevel Count Data Models Nelson, Tyler Song, Joon Jin Chin, Yoo-Mi Stamey, James D. Comput Math Methods Med Research Article 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. Hindawi 2018-02-25 /pmc/articles/PMC5845492/ /pubmed/29681994 http://dx.doi.org/10.1155/2018/3212351 Text en Copyright © 2018 Tyler Nelson et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Nelson, Tyler
Song, Joon Jin
Chin, Yoo-Mi
Stamey, James D.
Bayesian Correction for Misclassification in Multilevel Count Data Models
title Bayesian Correction for Misclassification in Multilevel Count Data Models
title_full Bayesian Correction for Misclassification in Multilevel Count Data Models
title_fullStr Bayesian Correction for Misclassification in Multilevel Count Data Models
title_full_unstemmed Bayesian Correction for Misclassification in Multilevel Count Data Models
title_short Bayesian Correction for Misclassification in Multilevel Count Data Models
title_sort bayesian correction for misclassification in multilevel count data models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5845492/
https://www.ncbi.nlm.nih.gov/pubmed/29681994
http://dx.doi.org/10.1155/2018/3212351
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