Bayesian Inference for Generalized Linear Mixed Model Based on the Multivariate t Distribution in Population Pharmacokinetic Study

Fang-Rong Yan, Yuan Huang, Jun-Lin Liu, Tao Lu and Jin-Guan Lin

PLOS ONE, 2013, vol. 8, issue 3, 1-10

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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0058369

DOI: 10.1371/journal.pone.0058369

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