Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays

Background: Genetic markers hold great promise for refining our ability to establish precise prognostic prediction for diseases. The development of comprehensive gene expression microarray technology has allowed the selection of relevant marker genes from a large pool of candidate genes in early-phased, developmental prognostic marker studies. The primary analytical task in such studies is to select a small fraction of relevant genes, typically from a list of significant genes, for further investigation in subsequent studies. Results: We develop a methodology for predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays. Key components in this methodology include building prediction models, assessing predictive performance of prediction models, and assessing significance of prediction results. As particular specifications, we assume Cox proportional hazard models with a compound covariate. For assessing predictive accuracy, we propos...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Shigeyuki Matsui
• Keywords: BMCBI 2006, Prediction, Prognostic, Prognostic Marker Studies