Open AccessBayesian Inference for Generalized Linear Mixed Model Based on the Multivariate t Distribution in Population Pharmacokinetic Study
Author(s) -
Fangrong Yan,
Yuan Huang,
Junlin Liu,
Tao Lü,
Jin-Guan Lin
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0058369
Subject(s) - multivariate statistics , bayesian inference , outlier , bayesian probability , computer science , population , inference , statistics , bayesian multivariate linear regression , generalized linear model , generalized linear mixed model , posterior probability , mathematics , artificial intelligence , linear regression , medicine , environmental health
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.