A comparison of methods for analysing logistic regression models with both clinical and genomic variables.
Résumé
Prediction from high-dimensional genomic data is an active field in todays medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies
suggest that combining clinical and genomic information may improve predictions. We consider methods that simultaneously use both types of
covariates, but applying dimension reduction only to the high-dimensional genomic variables. We first describe an approach based on partial least
square method in linear regession context and propose new approaches for logistic regression models. We perform a comparison of the performance
of several prediction methods combining clinical covariates and genomic data using simulation and a real data set.. Then, we illustrate their performances to classify two real data sets containing both clinical information and gene expression.
Domaines
Applications [stat.AP]
Origine : Fichiers produits par l'(les) auteur(s)