An evaluation of resampling methods for assessment of survival risk prediction in high-dimensional settings

Statistics in Medicine

Volumn 30, Issue 6, 2011, Pages 642-653

  Subramanian, Jyothi ^a   Simon, Richard ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Microarray data analysis; Prediction accuracy; Prognostic signatures; Resampling methods; Survival analysis

Indexed keywords

ACCURACY; ARTICLE; BOOTSTRAPPING; CANCER SURVIVAL; CONTROLLED STUDY; GENOMICS; INTERMETHOD COMPARISON; LARGE CELL LYMPHOMA; LUNG CANCER; MICROARRAY ANALYSIS; PREDICTION; PROGNOSIS; RESAMPLING; SAMPLING; SAMPLING ERROR; SIGNAL NOISE RATIO; SURVIVAL; SURVIVAL RISK PREDICTION; VALIDATION PROCESS;

ADENOCARCINOMA; AREA UNDER CURVE; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; HUMANS; LUNG NEOPLASMS; LYMPHOMA, LARGE B-CELL, DIFFUSE; MODELS, GENETIC; MODELS, STATISTICAL; RISK ASSESSMENT;

EID: 79951755788     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4106     Document Type: Article

References (36)

1. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland EB, Giltnane JM, Hurt EM, Zhao H, Averett L, Yang L, Wilson WH, Jaffe ES, Simon R, Klausner RD, Powell J, Duffey PL, Longo DL, Greiner TC, Weisenburger DD, Sanger WG, Dave BJ, Lynch JC, Vose J, Armitage JO, Montserrat E, López-Guillermo A, Grogan TM, Miller TP, LeBlanc M, Ott G, Kvaloy S, Delabie J, Holte H, Krajci P, Stokke T, Staudt LM; Lymphoma/Leukemia Molecular Profiling Project. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. The New England Journal of Medicine 2002; 346:1937-1947.
2. van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415:530-536.
3. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. The New England Journal of Medicine 2004; 351:2817-2826.
4. Vermeulen J, De Preter K, Naranjo A, Vercruysse L, Van Roy N, Hellemans J, Swerts K, Bravo S, Scaruffi P, Tonini GP, De Bernardi B, Noguera R, Piqueras M, Cañete A, Castel V, Janoueix-Lerosey I, Delattre O, Schleiermacher G, Michon J, Combaret V, Fischer M, Oberthuer A, Ambros PF, Beiske K, Bénard J, Marques B, Rubie H, Kohler J, Pötschger U, Ladenstein R, Hogarty MD, McGrady P, London WB, Laureys G, Speleman F, Vandesompele J. Predicting outcomes for children with neuroblastoma using a multigene-expression signature: a retrospective SIOPEN/COG/GPOH study. The Lancet Oncology 2009; 10:663-671.
5. Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute 2003; 95:14-18.
6. Clarke R, Ressom HW, Wang A, Xuan J, Liu MC, Gehan EA, Wang Y. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nature Reviews Cancer 2008; 8:37-49.
7. Cox DR. Regression models and life tables. Journal of the Royal Statistical Society, Series B 1972; 34:187-220.
8. Bair E, Hastie T, Paul D, Tibshirani R. Prediction by supervised principal components. Journal of the American Statistical Association 2006; 101:119-137.
9. Verweij PJM, Van Houwelingen HC. Penalized likelihood in Cox regression. Statistics in Medicine 1994; 13:2427-2436.
10. Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine 1997; 16:385-395.
11. Gui J, Li H. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 2005; 21:3001-3008.
12. Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics 2005; 21:3301-3307.
13. Potti A, Mukherjee S, Petersen R, Dressman HK, Bild A, Koontz J, Kratzke R, Watson MA, Kelley M, Ginsburg GS, West M, Harpole Jr DH, Nevins JR. A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. The New England Journal of Medicine 2006; 355:570-580.
14. Roepman P, Jassem J, Smit EF, Muley T, Niklinski J, van de Velde T, Witteveen AT, Rzyman W, Floore A, Burgers S, Giaccone G, Meister M, Dienemann H, Skrzypski M, Kozlowski M, Mooi WJ, van Zandwijk N. An immune response enriched 72-gene prognostic profile for early-stage non-small-cell lung cancer. Clinical Cancer Research 2009; 15:284-290.
15. Breslow NE. Contribution to the discussion on the paper by D.R. Cox. Journal of the Royal Statistical Society, Series B 1972; 34:216-217.
16. Hosmer Jr DW, Lemeshow S. Applied Survival Analysis. Wiley: New York, 1999; 93-105.
17. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology 2004; 159:882-890.
18. Lusa L, McShane LM, Radmacher MD, Shih JH, Wright GW, Simon R. Appropriateness of some resampling-based inference procedures for assessing performance of prognostic classifiers derived from microarray data. Statistics in Medicine 2007; 26:1102-1113.
19. Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000; 56:337-344.
20. Akritas MG. Nearest neighbor estimation of a bivariate distribution under random censoring. The Annals of Statistics 1994; 22:1299-1327.
21. Segal MR. Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited. Biostatistics 2006; 7:268-285.
22. Buyse M, Loi S, van't Veer L, Viale G, Delorenzi M, Glas AM, d'Assignies MS, Bergh J, Lidereau R, Ellis P, Harris A, Bogaerts J, Therasse P, Floore A, Amakrane M, Piette F, Rutgers E, Sotiriou C, Cardoso F, Piccart MJ; TRANSBIG Consortium. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. Journal of the National Cancer Institute 2006; 6:1183-1192.
23. Parker BJ, Günter S, Bedo J. Stratification bias in low signal microarray studies. BMC Bioinformatics 2007; 8:326-341.
24. Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma, Shedden K, Taylor JM, Enkemann SA, Tsao MS, Yeatman TJ, Gerald WL, Eschrich S, Jurisica I, Giordano TJ, Misek DE, Chang AC, Zhu CQ, Strumpf D, Hanash S, Shepherd FA, Ding K, Seymour L, Naoki K, Pennell N, Weir B, Verhaak R, Ladd-Acosta C, Golub T, Gruidl M, Sharma A, Szoke J, Zakowski M, Rusch V, Kris M, Viale A, Motoi N, Travis W, Conley B, Seshan VE, Meyerson M, Kuick R, Dobbin KK, Lively T, Jacobson JW, Beer DG. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nature Medicine 2008; 14:822-827.
25. Simon R, Lam A, Li MC, Ngan M, Menenzes S, Zhao Y. Analysis of gene expression data using BRB-ArrayTools. Cancer Informatics 2007; 2:11-17.
26. Zhao Y, Simon R. BRB Arraytools data archive for human cancer gene expression: a unique and efficient data sharing resource. Cancer Informatics 2008; 6:9-15.
27. Heagerty PJ, Saha P. survivalROC: Time-dependent ROC curve estimation from censored survival data. R package v. 1.0.0, 2006.
28. Therneau T, Lumley T. survival; Survival analysis, including penalised likelihood. R package v. 2.34-1, 2008.
29. Goeman JJ. L(1) penalized estimation in the Cox proportional hazards model. Biometrical Journal 2010; 52:70-84.
30. R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, 2008.
31. Tierney L, Rossini AJ, Sevcikova H. snow: simple network of workstations. R package v. 0.3-3, 2004.
32. Braga-Neto UM, Dougherty ER. Is cross-validation valid for small-sample microarray classification. Bioinformatics 2004; 20:374-380.
33. Xiao Y, Hua J, Dougherty ER. Quantification of the impact of feature selection on the variance of cross-validation error estimation. EURASIP Journal on Bioinformatics and Systems Biology 2007; 16354-16364.
34. Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. Journal of the National Cancer Institute 2007; 99:147-157.
35. Bøvelstad HM, Nygård S, Størvold HL, Aldrin M, Borgan Ø, Frigessi A, Lingjaerde OC. Predicting survival from microarray data-a comparative study. Bioinformatics 2007; 23:2080-2087.
36. Jiang W, Varma S, Simon R. Calculating confidence intervals for prediction error in microarray classification using resampling. Statistical Applications in Genetics and Molecular Biology 2008; 7:Article No. 8.