title: Gene selection in microarray survival studies under possibly non-proportional hazards.
abstract: BACKGROUND
  Univariate Cox regression (COX) is often used to select genes possibly linked to survival. With non-proportional hazards (NPH), COX could lead to under- or over-estimation of effects. The effect size measure c=P(T(1)<T(0)), i.e. the probability that a person randomly chosen from group G(1) dies earlier than a person from G(0), is independent of the proportional hazards (PH) assumption. Here we consider its generalization to continuous data c' and investigate the suitability of c' for gene selection.
  
  
  RESULTS
  Under PH, c' is most efficiently estimated by COX. Under NPH, c' can be obtained by weighted Cox regression (WHE) or a novel method, concordance regression (CON). The least biased and most stable estimates were obtained by CON. We propose to use c' as summary measure of effect size to rank genes irrespective of different types of NPH and censoring patterns.
  
  
  BACKGROUND
  WHE and CON are available as R packages.
  
  
  BACKGROUND
  georg.heinze@meduniwien.ac.at
  
  
  BACKGROUND
  Supplementary Data are available at Bioinformatics online.
authors.affiliation: Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.
authors.forename: Daniela
authors.initials: D
authors.lastname: Dunkler
authors.forename: Michael
authors.initials: M
authors.lastname: Schemper
authors.forename: Georg
authors.initials: G
authors.lastname: Heinze
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis; D014157:Transcription Factors / Q000235:genetics
chemical_list: D014157:Transcription Factors