title: Predicting survival from microarray data--a comparative study.
abstract: BACKGROUND
  Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Cox's proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso.
  
  
  RESULTS
  Statistical learning from subsets should be repeated several times in order to get a fair comparison between methods. Methods using coefficient shrinkage or linear combinations of the gene expression values have much better performance than the simple variable selection methods. For our data sets, ridge regression has the overall best performance.
  
  
  BACKGROUND
  Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.
authors.affiliation: Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no
authors.forename: H M
authors.initials: HM
authors.lastname: Bøvelstad
authors.forename: S
authors.initials: S
authors.lastname: Nygård
authors.forename: H L
authors.initials: HL
authors.lastname: Størvold
authors.forename: M
authors.initials: M
authors.lastname: Aldrin
authors.forename: Ø
authors.initials: Ø
authors.lastname: Borgan
authors.forename: A
authors.initials: A
authors.lastname: Frigessi
authors.forename: O C
authors.initials: OC
authors.lastname: Lingjaerde
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D005544:Forecasting; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000378:metabolism* / Q000401:mortality*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins