Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements

We investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from sedation measurement for Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT). Data for each patient is observed at different time points within the time up to 60 min. A model for the sed...

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Detalles Bibliográficos
Autores principales: Terzi, Erol, Cengiz, Mehmet Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722845/
https://www.ncbi.nlm.nih.gov/pubmed/23935702
http://dx.doi.org/10.1155/2013/579214
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author Terzi, Erol
Cengiz, Mehmet Ali
author_facet Terzi, Erol
Cengiz, Mehmet Ali
author_sort Terzi, Erol
collection PubMed
description We investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from sedation measurement for Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT). Data for each patient is observed at different time points within the time up to 60 min. A model for the sedation level of patients is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response, and then subsequent terms are introduced. To estimate the model, we use the Gibbs sampling given some appropriate prior distributions.
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spelling pubmed-37228452013-08-09 Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements Terzi, Erol Cengiz, Mehmet Ali Comput Math Methods Med Research Article We investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from sedation measurement for Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT). Data for each patient is observed at different time points within the time up to 60 min. A model for the sedation level of patients is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response, and then subsequent terms are introduced. To estimate the model, we use the Gibbs sampling given some appropriate prior distributions. Hindawi Publishing Corporation 2013 2013-07-10 /pmc/articles/PMC3722845/ /pubmed/23935702 http://dx.doi.org/10.1155/2013/579214 Text en Copyright © 2013 E. Terzi and M. A. Cengiz. https://creativecommons.org/licenses/by/3.0/ 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
Terzi, Erol
Cengiz, Mehmet Ali
Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements
title Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements
title_full Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements
title_fullStr Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements
title_full_unstemmed Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements
title_short Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements
title_sort bayesian hierarchical modeling for categorical longitudinal data from sedation measurements
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722845/
https://www.ncbi.nlm.nih.gov/pubmed/23935702
http://dx.doi.org/10.1155/2013/579214
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