A novel Gibbs maximum a posteriori (GMAP) approach on Bayesian nonlinear mixed-effects population pharmacokinetics (PK) models.
A novel Gibbs maximum a posteriori (GMAP) approach on Bayesian nonlinear mixed-effects population pharmacokinetics (PK) models.
复制标题
贝叶斯非线性混合效应群体药代动力学 (PK) 模型的新型吉布斯最大后验 (GMAP) 方法。
DOI：
10.1080/10543400902964159
复制
发表时间：
2009
影响因子：
1.1
通讯作者：
Li,Lang
中科院分区：
文献类型：
--
作者：
Kim,Seongho;Hall,StephenD;Li,Lang
本文比较了各种贝叶斯蒙特卡罗马尔可夫链（MCMC）方法和Gibbs最大后验（GMAP）算法在药代动力学（PK）研究中的非线性混合效应模型的实现。采用静脉注射双区室PK模型拟合咪达唑仑（MDZ）研究的PK数据，该研究招募了24名受试者，每个受试者有9个不同的时间点。构造了三阶段层次非线性混合模型。数据分析和模型性能比较表明，GMAP收敛速度快，结果可靠。同时，随机漫步大都会法采用了数据增强（DA）方法。数据分析表明，DA可以提高Random-walk Metropolis的收敛速度，但都不如GMAP快。通过Midazolam数据分析和仿真，比较了GMAP和各种MCMC算法的性能。
In this paper, various Bayesian Monte Carlo Markov chain (MCMC) methods and the proposed algorithm, the Gibbs maximum a posteriori (GMAP) algorithm, are compared for implementing the nonlinear mixed-effects model in pharmacokinetics (PK) studies. An intravenous two-compartmental PK model is adopted to fit the PK data from the midazolam (MDZ) studies, which recruited twenty-four individuals with nine different time points per subject. The three-stage hierarchical nonlinear mixed model is constructed. Data analysis and model performance comparisons show that GMAP converges the fastest and provides reliable results. At the mean time, data augmentation (DA) methods are used for the Random-walk Metropolis method. Data analysis shows that the speed of the convergence of Random-walk Metropolis can be improved by DA, but all of them are not as fast as GMAP. The performance of GMAP and various MCMC algorithms are compared through Midazolam data analysis and simulation.