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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 579214, 6 pages
Research Article

Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements

Department of Statistic, Ondokuz Mayis University, 55139 Samsun, Turkey

Received 20 December 2012; Accepted 17 June 2013

Academic Editor: Wenxiang Cong

Copyright © 2013 Erol Terzi and Mehmet Ali Cengiz. 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.


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.