[1] Yan Fangrong, , Zhang Ping, et al. Bayesian analysis for mixed-effects modeldefined by stochastic differential equations [J]. Journal of Southeast University (English Edition), 2014, 30 (1): 122-127. [doi:10.3969/j.issn.1003-7985.2014.01.023]

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Bayesian analysis for mixed-effects modeldefined by stochastic differential equations()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

30

Issue:

2014 1

Page:

122-127

Research Field:

Mathematics, Physics, Mechanics

Publishing date:

2014-03-31

Info

Title:

Bayesian analysis for mixed-effects modeldefined by stochastic differential equations

Author(s):

Yan Fangrong¹;  2;  4;  Zhang Ping²;  4;  Lu Tao³;  4;  Lin Jinguan¹

¹Department of Mathematics, Southeast University, Nanjing 210096, China
²Department of Mathematics, China Pharmaceutical University, Nanjing 210009, China
³Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 210009, China
⁴State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China

Keywords:

population pharmacokinetics;  mixed-effects models;  stochastic differential equations;  Bayesian analysis

PACS:

O212

DOI:

10.3969/j.issn.1003-7985.2014.01.023

Abstract:

The nonlinear mixed-effects model with stochastic differential equations(SDEs)is used to model the population pharmacokinetic(PPK)data that are extended from ordinary differential equations(ODEs)by adding a stochastic term to the state equation. Compared with the ODEs, the SDEs can model correlated residuals which are ubiquitous in actual pharmacokinetic problems. The Bayesian estimation is provided for nonlinear mixed-effects models based on stochastic differential equations. Combining the Gibbs and the Metropolis-Hastings algorithms, the population and individual parameter values are given through the parameter posterior predictive distributions. The analysis and simulation results show that the performance of the Bayesian estimation for mixed-effects SDEs model and analysis of population pharmacokinetic data is reliable. The results suggest that the proposed method is feasible for population pharmacokinetic data.

References:

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Memo

Memo:

Biographies: Yan Fangrong(1978—), male, doctor; Lin Jinguan(corresponding author), male, professor, jglin@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.11171065, 81130068), the Natural Science Foundation of Jiangsu Province(No.BK2011058), the Fundamental Research Funds for the Central Universities(No.JKPZ2013015).
Citation: Yan Fangrong, Zhang Ping, Lu Tao, et al.Bayesian analysis for mixed-effects model defined by stochastic differential equations[J].Journal of Southeast University(English Edition), 2014, 30(1):122-127.[doi:10.3969/j.issn.1003-7985.2014.01.023]

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