A PCA approach to population analysis: with application to a Phase II depression trial.
J Pharmacokinet Pharmacodyn
; 40(2): 213-27, 2013 Apr.
Article
em En
| MEDLINE
| ID: mdl-23504512
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Análise de Componente Principal
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Depressão
Tipo de estudo:
Clinical_trials
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article