CAJ | 학술논문

采用带有随机微分方程的非线性混合效应模型对群体药物代谢动力学数据建模，通过在状态方程中引入随机项，将常微分方程扩展到随机微分方程。和常微分方程相比，随机微分方程可解决群体药物代谢动力学模型中相关残差问题。利用贝叶斯估计对非线性混合效应随机微分方程模型参数进行估计，给出群体参数及个体参数的精确后验分布，将 Gibbs 和 Metropolis-Hastings 算法相结合，给出参数估计值。通过计算机模拟和实例分析验证了方法的可靠性，结果表明利用非线性混合效应随机微分方程模型及贝叶斯估计方法分析群体药物代谢动力学数据是可行的。
채용대유수궤미분방정적비선성혼합효응모형대군체약물대사동역학수거건모，통과재상태방정중인입수궤항，장상미분방정확전도수궤미분방정。화상미분방정상비，수궤미분방정가해결군체약물대사동역학모형중상관잔차문제。이용패협사고계대비선성혼합효응수궤미분방정모형삼수진행고계，급출군체삼수급개체삼수적정학후험분포，장 Gibbs 화 Metropolis-Hastings 산법상결합，급출삼수고계치。통과계산궤모의화실례분석험증료방법적가고성，결과표명이용비선성혼합효응수궤미분방정모형급패협사고계방법분석군체약물대사동역학수거시가행적。
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.