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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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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. |
format | Online Article Text |
id | pubmed-5845492 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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