Comput Math Methods Med - Bayesian hierarchical modeling for categorical longitudinal data from sedation measurements.

Tópicos

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Resumo

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 60min. 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.

Resumo Limpo

investig bayesian hierarch model analysi categor longitudin data sedat measur magnet reson imag mri computer tomographi ct data patient observ differ time point within time min model sedat level patient develop introduc first stage hierarch model multinomi model respons subsequ term introduc estim model use gibb sampl given appropri prior distribut

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