title: A PCA approach to population analysis: with application to a Phase II depression trial.
abstract: 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.
authors.affiliation: Department of Industrial and Information Engineering, University of Pavia, Via Ferrata 1, 27100, Pavia, Italy. eleonora.marostica@unipv.it
authors.forename: Eleonora
authors.initials: E
authors.lastname: Marostica
authors.forename: Alberto
authors.initials: A
authors.lastname: Russu
authors.forename: Roberto
authors.initials: R
authors.lastname: Gomeni
authors.forename: Stefano
authors.initials: S
authors.lastname: Zamuner
authors.forename: Giuseppe
authors.initials: G
authors.lastname: De Nicolao
mesh_terms: D003863:Depression / Q000188:drug therapy*; D004311:Double-Blind Method; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D011153:Population; D025341:Principal Component Analysis / Q000379:methods*; D012107:Research Design