Testing the additional predictive value of high-dimensional molecular data

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 underconsidered 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 very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to the two publicly available cancer data sets. Conclusions: Our simple and computa...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Anne-Laure Boulesteix, Torsten Hothorn
• Keywords: Additional Predictive Value, BMCBI 2010, Disease Outcome Prediction, Microarray Gene Expression