Sequential Interim Analyses of Survival Data in DNA Microarray Experiments

Background: Discovery of biomarkers that are correlated with therapy response and thus with survival is an important goal of medical research on severe diseases, e.g. cancer. Frequently, microarray studies are performed to identify genes of which the expression levels in pretherapeutic tissue samples are correlated to survival times of patients. Typically, such a study can take several years until the full planned sample size is available. Therefore, interim analyses are desirable, offering the possibility of stopping the study earlier, or of performing additional laboratory experiments to validate the role of the detected genes. While many methods correcting the multiple testing bias introduced by interim analyses have been proposed for studies of one single feature, there are still open questions about interim analyses of multiple features, particularly of high-dimensional microarray data, where the number of features clearly exceeds the number of samples. Therefore, we examine fals...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Andreas Leha, Tim Beißbarth, Klaus Jung
• Keywords: Artificial Intelligence, Average Power Rates, BMCBI 2011, Interim Analyses, Power Rate