title: A pseudo-R2 measure for selecting genomic markers with crossing hazards functions.
abstract: BACKGROUND
  In genomic medical studies, one of the major objectives is to identify genomic factors with a prognostic impact on time-to-event outcomes so as to provide new insights into the disease process. Selection usually relies on statistical univariate indices based on the Cox model. Such model assumes proportional hazards (PH) which is unlikely to hold for each genomic marker.
  
  
  METHODS
  In this paper, we introduce a novel pseudo-R2 measure derived from a crossing hazards model and designed for the selection of markers with crossing effects. The proposed index is related to the score statistic and quantifies the extent of a genomic factor to separate patients according to their survival times and marker measurements. We also show the importance of considering genomic markers with crossing effects as they potentially reflect the complex interplay between markers belonging to the same pathway.
  
  
  RESULTS
  Simulations show that our index is not affected by the censoring and the sample size of the study. It also performs better than classical indices under the crossing hazards assumption. The practical use of our index is illustrated in a lung cancer study. The use of the proposed pseudo-R2 allows the identification of cell-cycle dependent genes not identified when relying on the PH assumption.
  
  
  CONCLUSIONS
  The proposed index is a novel and promising tool for selecting markers with crossing hazards effects.
authors.affiliation: Genome Institute of Singapore, Biopolis, Singapore. sigrid.rouam@inserm.fr
authors.forename: Sigrid
authors.initials: S
authors.lastname: Rouam
authors.forename: Thierry
authors.initials: T
authors.lastname: Moreau
authors.forename: Philippe
authors.initials: P
authors.lastname: Broët
mesh_terms: D000465:Algorithms; D018287:Carcinoma, Large Cell / Q000235:genetics / Q000401:mortality; D003198:Computer Simulation; D018572:Disease-Free Survival; D056726:Genetic Association Studies / Q000379:methods / Q000706:statistics & numerical data*; D005819:Genetic Markers; D006801:Humans; D053208:Kaplan-Meier Estimate; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000235:genetics / Q000401:mortality; D016016:Proportional Hazards Models
chemical_list: D005819:Genetic Markers