Survival ensembles by the sum of pairwise differences with application to lung cancer microarray studies

Annals of Applied Statistics

Volumn 5, Issue 2 A, 2011, Pages 1081-1101

  Johnson, Brent A ^a   Long, Qi ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

Accelerated failure time; Boosting; Lasso; Proportional hazards regression; Survival analysis

Indexed keywords

EID: 84873372941     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/10-AOAS426     Document Type: Article

References (31)

1. Blanchard, G., Lugosi, G. and Vayatis, N. (2004). On the rate of regularized boosting classifiers. J. Mach. Learn. Res. 4 861-894.Mathematical Reviews (MathSciNet): MR2076000 Zentralblatt MATH: 1083.68109
2. Breiman, L. (1998). Arcing classifiers (with discussion). Ann. Statist. 26 801-849.Mathematical Reviews (MathSciNet): MR1635406 Zentralblatt MATH: 0934.62064 Digital Object Identifier: doi:10.1214/aos/1024691079 Project Euclid: euclid.aos/1024691079
3. Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting (with discussion). Statist. Sci. 22 477-522.Mathematical Reviews (MathSciNet): MR2420454 Digital Object Identifier: doi:10.1214/07-STS242 Project Euclid: euclid.ss/1207580163
4. 2 loss: Regression and classification. J. Amer. Statist. Assoc. 98 324-339.Mathematical Reviews (MathSciNet): MR1995709 Zentralblatt MATH: 1041.62029 Digital Object Identifier: doi:10.1198/016214503000125
5. Cox, D. R. (1972). Regression models and life-tables (with discussion). J. Roy. Statist. Soc. Ser. B 34 187-202.Mathematical Reviews (MathSciNet): MR341758
6. Cox, D. R. and Oakes, D. (1984). Analysis of Survival Data. Chapman & Hall, London.Mathematical Reviews (MathSciNet): MR751780 Zentralblatt MATH: 0900.62605
7. Fan, J. and Li, R. (2002). Variable selection for Cox's proportional hazards model and frailty model. Ann. Statist. 30 74-99.Mathematical Reviews (MathSciNet): MR1892656 Zentralblatt MATH: 1012.62106 Digital Object Identifier: doi:10.1214/aos/1015362185 Project Euclid: euclid.aos/1015362185
8. Freund, Y. and Schapire, R. (1996). Experiments with a new boosting algorithm. In Proceedings of the 13th International Conference on Machine Learning Theory. Morgan Kaufmann, San Francisco, CA.
9. Freund, Y. and Schapire, R. (1997). A decision-theoretic generalization on on-line learning and an application to boosting. J. Comput. System Sci. 55 119-139.Mathematical Reviews (MathSciNet): MR1473055 Zentralblatt MATH: 0880.68103 Digital Object Identifier: doi:10.1006/jcss.1997.1504
10. Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Ann. Statist. 29 1189-1232.Mathematical Reviews (MathSciNet): MR1873328 Zentralblatt MATH: 1043.62034 Digital Object Identifier: doi:10.1214/aos/1013203451 Project Euclid: euclid.aos/1013203451
11. Friedman, J., Hastie, T. and Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion). Ann. Statist. 28 337-407.Mathematical Reviews (MathSciNet): MR1790002 Zentralblatt MATH: 1106.62323 Digital Object Identifier: doi:10.1214/aos/1016218223 Project Euclid: euclid.aos/1016218223
12. Gehan, E. A. (1965). A generalized Wilcoxon test for comparing arbitrarily single-censored samples. Biometrika 52 203-223.Mathematical Reviews (MathSciNet): MR207130 Zentralblatt MATH: 0133.41901
13. Grambsch, P. M. and Therneau, T. M. (1994). Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81 515-526.Mathematical Reviews (MathSciNet): MR1311094 Zentralblatt MATH: 0810.62096 Digital Object Identifier: doi:10.1093/biomet/81.3.515
14. Hothorn, T. and Bühlmann, P. (2007). Mboost: Model-based boosting. R package version 0.5-8. Available at http://CRAN.R-project.org/.
15. Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A. and van der Laan, M. (2006). Survival ensembles. Biostatistics 7 355-373.
16. Jaeckel, L. A. (1972). Estimating regression coefficients by minimizing the dispersion of the residuals. Ann. Math. Statist. 43 1449-1458.Mathematical Reviews (MathSciNet): MR348930 Zentralblatt MATH: 0277.62049 Digital Object Identifier: doi:10.1214/aoms/1177692377 Project Euclid: euclid.aoms/1177692377
17. Johnson, B. A., Lin, D. Y. and Zeng, D. (2008). Penalized estimating functions and variable selection in semiparametric regression models. J. Amer. Statist. Assoc. 103 672-680.Mathematical Reviews (MathSciNet): MR2435469 Zentralblatt MATH: 05564520 Digital Object Identifier: doi:10.1198/016214508000000184
18. Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data. Wiley, New York.Mathematical Reviews (MathSciNet): MR1924807
19. Lin, D. Y., Wei, L. J. and Ying, Z. (1993). Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika 80 557-581.Mathematical Reviews (MathSciNet): MR1248021 Zentralblatt MATH: 0788.62094 Digital Object Identifier: doi:10.1093/biomet/80.3.557
20. Morris, C. N., Norton, E. C. and Zhou, X. H. (1994). Parametric duration analysis of nursing home usage. In Case Studies in Biometry (N. Lange, L. Ryan, L. Biilard, D. Brillinger, L. Conquest and J. Greenhouse, eds.) 231-248. Wiley, New York.
21. Morris, J. S., Yin, G., Baggerly, K. A., Wu, C. and Zhang, L. (2005). Pooling information across different studies and oligonucleotide microarray chip types to identify prognostic genes for lung cancer. In Methods of Microarrray Data Analysis IV (J. S. Shoemaker and S. M. Lin, eds.). Springer, New York.
22. Prentice, R. L. (1978). Linear rank tests with right-censored data. Biometrika 65 167-179.Mathematical Reviews (MathSciNet): MR497517 Zentralblatt MATH: 0377.62024 Digital Object Identifier: doi:10.1093/biomet/65.1.167
23. Reid, N. (1994). A conversation with Sir David Cox. Statist. Sci. 9 439-455.Mathematical Reviews (MathSciNet): MR1325436 Digital Object Identifier: doi:10.1214/ss/1177010394 Project Euclid: euclid.ss/1177010394
24. Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics 31 172-181.
25. Schoenfeld, D. (1982). Partial residuals for the proportional hazards regression model. Biometrika 69 239-241.
26. Street, W. N., Mangasarian, O. L. and Wolberg, W. H. (1995). An inductive learning approach to prognostic prediction. In Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA.
27. Therneau, T. M. and Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York.Mathematical Reviews (MathSciNet): MR1774977 Zentralblatt MATH: 0958.62094
28. Tsiatis, A. A. (1990). Estimating regression parameters using linear rank tests for censored data. Ann. Statist. 18 354-372.Mathematical Reviews (MathSciNet): MR1041397 Zentralblatt MATH: 0701.62051 Digital Object Identifier: doi:10.1214/aos/1176347504 Project Euclid: euclid.aos/1176347504
29. Tsiatis, A. A. (2006). Semiparametric Theory and Missing Data. Springer, New York.Mathematical Reviews (MathSciNet): MR2233926 Zentralblatt MATH: 1105.62002
30. van der Laan, M. J. and Robins, J. M. (2003). Unified Methods for Censored Longitudinal Data and Causality. Springer, New York.Mathematical Reviews (MathSciNet): MR1958123 Zentralblatt MATH: 1013.62034
31. Zhang, T. and Yu, B. (2005). Boosting with early stopping: Convergence and consistency. Ann. Statist. 33 1538-1579.Mathematical Reviews (MathSciNet): MR2166555 Zentralblatt MATH: 1078.62038 Digital Object Identifier: doi:10.1214/009053605000000255 Project Euclid: euclid.aos/1123250222