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

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

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.

Suggested Citation

  • Fang-Rong Yan & Yuan Huang & Jun-Lin Liu & Tao Lu & Jin-Guan Lin, 2013. "Bayesian Inference for Generalized Linear Mixed Model Based on the Multivariate t Distribution in Population Pharmacokinetic Study," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-10, March.
    • Handle: RePEc:plo:pone00:0058369
    DOI: 10.1371/journal.pone.0058369
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    References listed on IDEAS

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    1. Ruth Salway & Jon Wakefield, 2008. "Gamma Generalized Linear Models for Pharmacokinetic Data," Biometrics, The International Biometric Society, vol. 64(2), pages 620-626, June.
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