title: Separate class true discovery rate degree of association sets for biomarker identification.
abstract: In 2008, Efron showed that biological features in a high-dimensional study can be divided into classes and a separate false discovery rate (FDR) analysis can be conducted in each class using information from the entire set of features to assess the FDR within each class. We apply this separate class approach to true discovery rate degree of association (TDRDA) set analysis, which is used in clinical-genomic studies to identify sets of biomarkers having strong association with clinical outcome or state while controlling the FDR. Careful choice of classes based on prior information can increase the identification power of the separate class analysis relative to the overall analysis.
authors.affiliation: a Department of Biostatistics , Genomic Health, Inc. , Redwood City , California , USA.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Crager
authors.forename: Murat
authors.initials: M
authors.lastname: Ahmed
mesh_terms: D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000601:surgery; D003198:Computer Simulation; D005189:False Positive Reactions; D005260:Female; D015870:Gene Expression; D056726:Genetic Association Studies / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis / Q000235:genetics; D011336:Probability; D011379:Prognosis; D016016:Proportional Hazards Models
chemical_list: D015415:Biomarkers
keywords: False discovery rate; Separate class; TDRDA set; True discovery rate degree of association set