title: Bayesian inference for generalized linear mixed model based on the multivariate t distribution in population pharmacokinetic study.
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.
authors.affiliation: Department of Mathematics, Southeast University, Nanjing, China. f.r.yan@163.com
authors.forename: Fang-Rong
authors.initials: FR
authors.lastname: Yan
authors.forename: Yuan
authors.initials: Y
authors.lastname: Huang
authors.forename: Jun-Lin
authors.initials: JL
authors.lastname: Liu
authors.forename: Tao
authors.initials: T
authors.lastname: Lu
authors.forename: Jin-Guan
authors.initials: JG
authors.lastname: Lin
mesh_terms: D001499:Bayes Theorem; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D010599:Pharmacokinetics