%0 Journal Article
%T Bayesian Inference for Generalized Linear Mixed Model Based on the Multivariate t Distribution in Population Pharmacokinetic Study
%A Fang-Rong Yan
%A Yuan Huang
%A Jun-Lin Liu
%A Tao Lu
%A Jin-Guan Lin
%J PLOS ONE
%D 2013
%I Public Library of Science (PLoS)
%R 10.1371/journal.pone.0058369
%X 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.
%U http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058369