Testing the additional predictive value of high-dimensional molecular data.

ABSTRACT:

Background

While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.

Results

We suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations). In simulations, our novel approach is found to have v

SUBMITTER: Boulesteix AL 

PROVIDER: S-EPMC2837029 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature