Assessment of evaluation criteria for survival prediction from genomic data

Biometrical Journal

Volumn 53, Issue 2, 2011, Pages 202-216

  Bøvelstad, Hege M ^a,b   Borgan, Ørnulf ^a  

^a UNIVERSITY OF OSLO   (Norway)

^b UNIVERSITY OF TROMSØ   (Norway)

Author keywords

AUC; Brier Score; Cox regression; Explained variation; Microarray gene expression data

Indexed keywords

GENE EXPRESSION; LEAST SQUARES APPROXIMATIONS; REGRESSION ANALYSIS;

AUC; BRIER SCORE; COMPARATIVES STUDIES; COX REGRESSION; EVALUATION CRITERIA; EXPLAINED VARIATION; GENOMIC DATA; MICROARRAY GENE EXPRESSION DATA; REGULARIZATION METHODS; SURVIVAL PREDICTION;

FORECASTING;

EID: 79952261591     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201000048     Document Type: Article

References (39)

1. Akritas, M. G. (1994). Nearest neighbor estimation of a bivariate distribution under random censoring. The Annals of Statistics 22, 1299-1327.
2. Bair, E., Hastie, T., Paul, D. and Tibshirani, R. (2006). Prediction by supervised principal components. Journal of the American Statistical Association 101, 119-137.
3. Bair, E. and Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene expression data. PLoS Biology 2, 511-522.
4. Bøvelstad, H. M., Nygård, S. and Borgan, Ø. (2009). Survival prediction from clinico-genomic models - a comparative study. BMC Bioinformatics 10, 413.
5. Bøvelstad, H. M., Nygård, S., Størvold, H. L., Aldrin, M., Borgan, Ø., Frigessi, A. and Lingjærde, O. C. (2007). Predicting survival from microarray data - a comparative study. Bioinformatics 23, 2080-2087.
6. Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society: Series B (Statistical Methodology) 34, 187-220.
7. Datta, S., Le-Rademacher, J. and Datta, S. (2007). Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and lasso. Biometrics 63, 259-271.
8. Graf, E., Schmoor, C., Sauerbrei, W. and Schumacher, M. (1999). Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine 18, 2529-2545.
9. Gui, J. and Li, H. (2005). Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 21, 3001-3008.
10. Hanley, J. A. (2005). Receiver operating characteristic (ROC) curves. In: Armitage, P. and Colton, T. (Eds.). Encyclopedia of Biostatistics. Wiley, Chichester, 4523-4529.
11. Hastie, T., Tibshirani, R. and Friedman, J. (2009). Elements of Statistical Learning, Data Mining, Inference, and Prediction (2 edn). Springer, New York.
12. Heagerty, P. J., Lumley, T. and Pepe, M. S. (2000). Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337-344.
13. Henderson, R. and Keiding, N. (2005). Individual survival time prediction using statistical models. Clinical Ethics 31, 703-706.
14. Hielscher, T., Zucknick, M., Werft, W. and Benner, A. (2010). On the prognostic value of survival models with application to gene expression signatures. Statistics in Medicine 29, 818-829.
15. Huang, J., Ma, S. and Xie, H. (2006). Regularized estimation in the accelerated failure time model with high-dimensional covariates. Biometrics 62, 813-820.
16. Klein, J. P. and Moeschberger, M. L. (2003). Survival Analysis. Techniques for Censored and Truncated Data (2 edn). Springer, New York.
17. Ma, S. and Huang, J. (2007). Additive risk survival model with microarray data. BMC Bioinformatics 8, 192.
18. Ma, S., Kosorok, M. R. and Fine, J. P. (2006). Additive risk models for survival data with high-dimensional covariates. Biometrics 62, 202-210.
19. Martinussen, T. and Scheike, T. H. (2009). The additive hazards model with high-dimensional regressors. Lifetime Data Analysis 15, 330-342.
20. Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika 78, 691-692.
21. Nygård, S., Borgan, Ø., Lingjærde, O. C. and Størvold, H. L. (2008). Partial least squares Cox regression for genome-wide data. Lifetime Data Analysis 14, 179-195.
22. Oberthür, A., Kaderali, L., Kahlert, Y., Hero, B., Westermann, F., Berthold, F., Brors, B., Eils, R. and Fischer, M. (2008). Subclassification and individual survival time prediction from gene expression data of neuroblastoma patients by using CASPAR. Clinical Cancer Research 14, 6590-6601.
23. Park, M. Y. and Hastie, T. (2007). L1-regularization path algorithm for generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, 659-677.
24. Porzelius, C., Schumacher, M. and Binder, H. (2010). A general, prediction error-based criterion for selecting model complexity for high-dimensional survival models. Statistics in Medicine 23, 830-838.
25. Rosenwald, M., Wright, G., Chan, W. C., Connors, J. M., Campo, E., Fisher, R. I., Gascoyne, R. D., Muller-Hermelink, H. K., Smeland, E. B. and Staudt, L. M. (2002). The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. The New England Journal of Medicine 346, 1937-1947.
26. Schemper, M. and Stare, J. (1996). Explained variation in survival analysis. Statistics in Medicine 15, 1999-2012.
27. Schumacher, M., Binder, H. and Gerds, T. (2007). Assessment of survival prediction models based on microarray data. Bioinformatics 23, 1768-1774.
28. Segal, M. R. (2006). Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited. Biostatistics 7, 268-285.
29. Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., Nordgren, H., Farmer, P., Praz, V., Haibe-Kains, B., Desmedt, C., Larsimont, D., Cardoso, F., Peterse, H., Nuyten, D., Buyse, M., van de Vijver, M. J., Bergh, J., Piccart, M. T. and Delorenzi, M. (2006). Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. Journal of the National Cancer Institute 98, 262-272.
30. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 58, 267-288.
31. Tibshirani, R. (1997). The lasso method for variable selection in the Cox model. Statistics in Medicine 16, 385-395.
32. van de Vijver, M. J., He, Y. D., van 't Veer, L. J., Dai, H., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E. T., Friend, S. H. and Bernards, R. (2002). A gene-expression signature as a predictor of survival in breast cancer. The New England Journal of Medicine 347, 1999-2009.
33. van Houwelingen, H. C., Bruinsma, T., Hart, A. A. M., van 't Veer, L. J. and Wessels, L. F. A. (2006). Cross-validated Cox regression on microarray gene expression data. Statistics in Medicine 25, 3201-3216.
34. van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., Schreiber, G. J., Kerkhoven, R. M., Roberts, C., Linsley, P. S., Bernards, R. and Friend, S. H. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-536.
35. van Wieringen, W., Kun, D., Hampel, R. and Boulesteix, A. (2009). Survival prediction using gene expression data: a review and comparison. Computational Statistics and Data Analysis 53, 1590-1603.
36. Verweij, P. J. M. and van Houwelingen, H. C. (1993). Cross-validation in survival analysis. Statistics in Medicine 12, 2305-2314.
37. Verweij, P. J. M. and van Houwelingen, H. C. (1994). Penalized likelihood in Cox regression. Statistics in Medicine 13, 2427-2436.
38. Wang, Y. X., Klijn, J. G. M., Zhang, Y., Sieuwerts, A., Look, M. P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M. E., Yu, J., Jatkoe, T., Berns, E. M. J. J., Atkins, D. and Foekens, J. A. (2005). Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671-679.
39. Witten, D. M. and Tibshirani, R. (2010). Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 19, 29-51.