Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-07-10
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Terzi, Erol& Cengiz, Mehmet Ali. 2013. Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-482296
Modern Language Association (MLA)
Terzi, Erol& Cengiz, Mehmet Ali. Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-482296
American Medical Association (AMA)
Terzi, Erol& Cengiz, Mehmet Ali. Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-482296
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-482296