| Autor: |
Fang-Rong Yan, Yuan Huang, Jun-Lin Liu, Tao Lu, Jin-Guan Lin |
| Jazyk: |
angličtina |
| Rok vydání: |
2013 |
| Předmět: |
|
| Zdroj: |
PLoS ONE, Vol 8, Iss 3, p e58369 (2013) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0058369 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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