Department of Industrial and Information Engineering, University of Pavia, Via Ferrata 1, 27100, Pavia, Italy.
J Pharmacokinet Pharmacodyn. 2013 Apr;40(2):213-27. doi: 10.1007/s10928-013-9304-6. Epub 2013 Mar 17.
For psychiatric diseases, established mechanistic models are lacking and alternative empirical mathematical structures are usually explored by a trial-and-error procedure. To address this problem, one of the most promising approaches is an automated model-free technique that extracts the model structure directly from the statistical properties of the data. In this paper, a linear-in-parameter modelling approach is developed based on principal component analysis (PCA). The model complexity, i.e. the number of components entering the PCA-based model, is selected by either cross-validation or Mallows' Cp criterion. This new approach has been validated on both simulated and clinical data taken from a Phase II depression trial. Simulated datasets are generated through three parametric models: Weibull, Inverse Bateman and Weibull-and-Linear. In particular, concerning simulated datasets, it is found that the PCA approach compares very favourably with some of the popular parametric models used for analyzing data collected during psychiatric trials. Furthermore, the proposed method performs well on the experimental data. This approach can be useful whenever a mechanistic modelling procedure cannot be pursued. Moreover, it could support subsequent semi-mechanistic model building.
对于精神疾病，缺乏既定的机械模型，通常通过试错程序探索替代的经验数学结构。为了解决这个问题，最有前途的方法之一是一种自动化的无模型技术，它可以直接从数据的统计特性中提取模型结构。本文提出了一种基于主成分分析（PCA）的线性参数建模方法。通过交叉验证或马罗夫的 Cp 准则选择模型复杂度，即进入基于 PCA 的模型的组件数量。该新方法已在来自 II 期抑郁症试验的模拟和临床数据上进行了验证。通过三个参数模型生成模拟数据集：Weibull、Inverse Bateman 和 Weibull-and-Linear。特别是，关于模拟数据集，发现 PCA 方法与一些用于分析在精神病学试验中收集的数据的流行参数模型相比具有很大优势。此外，该方法在实验数据上表现良好。只要不能进行机械建模过程，这种方法就很有用。此外，它可以支持后续的半机械模型构建。
