Parameter Estimation of Population Pharmacokinetic Models with Stochastic Differential Equations: Implementation of an Estimation Algorithm. (10th November 2014)

• Record Type: Journal Article
• Authors: Yan, Fang-Rong; Zhang, Ping; Liu, Jun-Lin; Tao, Yu-Xi; Lin, Xiao; Lu, Tao; Lin, Jin-Guan
• Other Names: Su Steve Academic Editor.
• Is Part Of: Journal of probability and statistics. Volume 2014(2014)
• Publication Date: 2014-11-10
• Subjects: Probabilities -- Periodicals; Mathematical statistics -- Periodicals; Mathematical statistics; Probabilities; Periodicals; 519
• DOI: 10.1155/2014/836518
• Languages: English
• ISSNs: 1687-952X

Abstract

Abstract : Population pharmacokinetic (PPK) models play a pivotal role in quantitative pharmacology study, which are classically analyzed by nonlinear mixed-effects models based on ordinary differential equations. This paper describes the implementation of SDEs in population pharmacokinetic models, where parameters are estimated by a novel approximation of likelihood function. This approximation is constructed by combining the MCMC method used in nonlinear mixed-effects modeling with the extended Kalman filter used in SDE models. The analysis and simulation results show that the performance of the approximation of likelihood function for mixed-effects SDEs model and analysis of population pharmacokinetic data is reliable. The results suggest that the proposed method is feasible for the analysis of population pharmacokinetic data.