Wang Yi, Eskridge Kent M, Zhang Shunpu
Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583-0963, USA.
J Pharmacokinet Pharmacodyn. 2008 Aug;35(4):443-63. doi: 10.1007/s10928-008-9096-2. Epub 2008 Sep 10.
Motivated by the use of semiparametric nonlinear mixed-effects modeling on longitudinal data, we develop a new semiparametric modeling approach to address potential structural model misspecification for population pharmacokinetic/pharmacodynamic (PK/PD) analysis. Specifically, we use a set of ordinary differential equations (ODEs) with form dx/dt = A(t)x + B(t) where B(t) is a nonparametric function that is estimated using penalized splines. The inclusion of a nonparametric function in the ODEs makes identification of structural model misspecification feasible by quantifying the model uncertainty and provides flexibility for accommodating possible structural model deficiencies. The resulting model will be implemented in a nonlinear mixed-effects modeling setup for population analysis. We illustrate the method with an application to cefamandole data and evaluate its performance through simulations.
受纵向数据半参数非线性混合效应建模应用的启发,我们开发了一种新的半参数建模方法,以解决群体药代动力学/药效学(PK/PD)分析中潜在的结构模型错误设定问题。具体而言,我们使用一组形式为dx/dt = A(t)x + B(t)的常微分方程(ODE),其中B(t)是一个使用惩罚样条估计的非参数函数。在ODE中包含非参数函数通过量化模型不确定性使结构模型错误设定的识别变得可行,并为适应可能的结构模型缺陷提供了灵活性。所得模型将在用于群体分析的非线性混合效应建模设置中实现。我们通过应用头孢孟多数据来说明该方法,并通过模拟评估其性能。