Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Department of Pharmacy Practice, Helwan University, Cairo, Egypt.
AAPS J. 2019 Feb 27;21(3):34. doi: 10.1208/s12248-019-0305-2.
Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFV). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method's covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.
非线性混合效应模型被广泛用于描述纵向数据，以提高药物开发过程的效率或增加对所研究疾病的理解。在这种情况下，建模假设的适当性对于得出正确的结论至关重要，并且必须仔细评估是否存在实质性违反。在这里，我们提出了一种基于条件加权残差（CWRES）评估偏差的新结构模型评估方法。我们通过评估葡萄糖稳态的两个集成模型，即整合葡萄糖-胰岛素（IGI）模型和整合最小模型（IMM）的预测偏差，来说明这种方法。然后，从每个模型中模拟一个数据集，然后用两个模型进行分析。对每个模型拟合输出的 CWRES 进行建模，以捕获 CWRES 中的系统趋势以及结构模型在目标函数值差异（ΔOFV）方面的指定错误的程度。CWRES 偏差的估计值用于通过逆一阶条件估计方法的协方差方程来计算条件预测的相应偏差。时间、葡萄糖和胰岛素浓度预测是研究的自变量。当这种偏差发生时，新方法正确识别了整合最小模型（IMM）中葡萄糖子模型的偏差，并计算了由此产生的偏差的绝对值和比例值。CWRES 偏差与自变量之间的关系很好地符合了指定错误的真实趋势。该方法是一种快速、易于自动化的模型开发/评估过程诊断工具，已经作为 Perl-speaks-NONMEM 软件的一部分实现。
