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Use of a linearization approximation facilitating stochastic model building

机译:线性化近似的使用有助于随机模型的建立

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The objective of this work was to facilitate the development of nonlinear mixed effects models by establishing a diagnostic method for evaluation of stochastic model components. The random effects investigated were between subject, between occasion and residual variability. The method was based on a first-order conditional estimates linear approximation and evaluated on three real datasets with previously developed population pharmacokinetic models. The results were assessed based on the agreement in difference in objective function value between a basic model and extended models for the standard nonlinear and linearized approach respectively. The linearization was found to accurately identify significant extensions of the model's stochastic components with notably decreased runtimes as compared to the standard nonlinear analysis. The observed gain in runtimes varied between four to more than 50-fold and the largest gains were seen for models with originally long runtimes. This method may be especially useful as a screening tool to detect correlations between random effects since it substantially quickens the estimation of large variance-covariance blocks. To expedite the application of this diagnostic tool, the linearization procedure has been automated and implemented in the software package PsN.
机译:这项工作的目的是通过建立一种评估随机模型成分的诊断方法来促进非线性混合效应模型的开发。所研究的随机效应是在受试者之间,场合与残余变异之间。该方法基于一阶条件估计线性近似,并使用先前开发的总体药代动力学模型在三个真实数据集上进行了评估。基于分别针对标准非线性方法和线性方法的基本模型和扩展模型之间的目标函数值差异的一致性来评估结果。与标准非线性分析相比,发现线性化可以准确地识别模型的随机组件的显着扩展,并且运行时间显着减少。在运行时中观察到的增益在四到50倍之间变化,对于最初运行时较长的模型,可以看到最大的增益。该方法作为检测随机效应之间的相关性的筛选工具可能特别有用,因为它可以大大加快对大方差-协方差块的估计。为了加快该诊断工具的应用,线性化过程已自动化并在软件包PsN中实现。

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