Bayesian Inference for Generalized Linear Mixed Model Based on the Multivariate t Distribution in Population Pharmacokinetic Study |
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| Authors: | Fang-Rong Yan Yuan Huang Jun-Lin Liu Tao Lu Jin-Guan Lin |
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| Affiliation: | 1. Department of Mathematics, Southeast University, Nanjing, China.; 2. Department of Mathematics, China Pharmaceutical University, Nanjing, China.; 3. State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.; 4. Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing, China.; Sapienza University of Rome, Italy, |
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| Abstract: | This article provides a fully Bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK) model. To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed distribution. The aim of this study is to investigate and implement the performance of the multivariate t distribution to analyze population pharmacokinetic data. Bayesian predictive inferences and the Metropolis-Hastings algorithm schemes are used to process the intractable posterior integration. The precision and accuracy of the proposed model are illustrated by the simulating data and a real example of theophylline data. |
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