Parameter estimation of population pharmacokinetic models with stochastic differential equations: implementation of an estimation algorithm
type: Item
instance of: scholarly article
title: Parameter estimation of population pharmacokinetic models with stochastic differential equations: implementation of an estimation algorithm
zbMATH Open document ID: 1426.62331
DOI: 10.1155/2014/836518
author: Fangrong Yan
author: Ping Zhang
author: Jun-Lin Liu
author: Yu-Xi Tao
author: Xiao Lin
author: Jinguan Lin
author: Tao Lü
published in: Journal of Probability and Statistics
publication date: 2018-08-28T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6928181
Wikidata QID: Q59061426
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/836518
OpenAlex ID: W2077761139
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description: scientific article; zbMATH DE number 6928181