Random survival forests for high-dimensional data

Statistical Analysis and Data Mining

Volumn 4, Issue 1, 2011, Pages 115-132

  Ishwaran, Hemant ^a   Kogalur, Udaya B ^a   Chen, Xi ^b   Minn, Andy J ^c  

^a LERNER RESEARCH INSTITUTE   (United States)

^b VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Forests; Maximal subtree; Minimal depth; Trees; Variable selection; VIMP

Indexed keywords

DECISION TREES; FORESTRY;

FOREST; HIGH DIMENSIONAL DATA; MAXIMAL SUBTREE; MINIMAL DEPTH; RANDOM FORESTS; SUB TREES; SURVIVAL FORESTS; TREE; VARIABLES SELECTIONS; VIMP;

CLUSTERING ALGORITHMS;

EID: 79551713057     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10103     Document Type: Article

References (32)

1. D. Nguyen and D. M. Rocke, Partial least squares proportional hazard regression for application to DNA microarray data, Bioinformatics 18 (2002), 1625-1632.
2. H. Z. Li and J. Gui, Partial Cox regression analysis for high-dimensional microarray gene expression data, Bioinformatics 20 (2004), 208-215.
3. E. Bair and R. Tibshirani, Semi-supervised methods to predict patient survival from gene expression data, PLoS Biol 2 (2004), 0511-0522.
4. 1-regularization path algorithm for generalized linear models, JRSSB 69 (2007), 659-677.
5. H. H. Zhang and W. Lu, Adaptive Lasso for Cox's proportional hazards model, Biometrika 94 (2007), 691-703.
6. H. Li and Y. Luan, Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data, Bioinformatics 21 (2006), 2403-2409.
7. S. Ma and J. Huang, Clustering threshold gradient descent regularization: with applications to microarray studies, Bioinformatics 23 (2006), 466-472.
8. T. Hothorn and P. Buhlmann, Model-based boosting in high-dimensions, Bioinformatics 22 (2006), 2828-2829.
9. B. Binder and M. Schumacher, Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models, BMC Bioinform 9 (2008), 14.
10. G. Ridgeway, The state of boosting, Comput Sci Stat 31 (1999), 172-181.
11. L. Breiman, Random forests, Mach Learn 45 (2001), 5-32.
12. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, California, Belmont, 1984.
13. L. Breiman, Bagging predictors, Mach Learn 26 (1996), 123-140.
14. L. Breiman, Heuristics of instability and stabilization in model selection, Ann Stat 24 (1996), 2350-2383.
15. A. Liaw and M. Wiener randomForest 4.5-36. R package, 2010, .
16. K. L. Lunetta, L. B. Hayward, J. Segal, and P. V. Eerdewegh, Screening large-scale association study data: exploiting interactions using random forests, BMC Genet 5 (2004), 32.
17. A. Bureau, J. Dupuis, K. Falls, K. L. Lunetta, B. Hayward, T. P. Keith, and P. V. Eerdewegh, Identifying SNPs predictive of phenotype using random forests, Genet Epidemiol 28 (2005), 171-182.
18. R. Diaz-Uriarte and S. Alvarez de Andres, Gene selection and classification of microarray data using random forest, BMC Bioinform 7 (2006), 3.
19. H. Ishwaran, U. B. Kogalur, E. H. Blackstone, and M. S. Lauer, Random survival forests, Ann Appl Stat 2 (2008), 841-860.
20. H. Ishwaran and U. B. Kogalur, Random survival forests for R, Rnews 7/2 (2007), 25-31.
21. H. Ishwaran and U. B. Kogalur, RandomSurvivalForest: Random Survival Forests. R package version 3.6.3, 2010, .
22. H. Ishwaran, U. B. Kogalur, E. Z. Gorodeski, A. J. Minn, and M. S. Lauer, High-dimensional variable selection for survival data, J Am Stat Assoc 105 (2010), 205-217.
23. H. Ishwaran, Variable importance in binary regression trees and forests, Electron J Stat 1 (2007), 519-537.
24. H. Ishwaran, U. B. Kogalur, R. D. Moore, S. J. Gange, and B. M. Lau, Random survival forests for competing risks (submitted), 2010.
25. R. Genuer, J.-M. Poggi, and C. Tuleau, Random Forests: some methodological insights, ArXiv e-prints, 0811.3619, 2008.
26. M. R. Segal, Regression trees for censored data, Biometrics 44 (1988), 35-47.
27. P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Mach Learn 63 (2006), 3-42.
28. D. Amaratunga, J. Cabrera, and Y.-S. Lee, Enriched random forests, Bioinformatics 24(18) (2008), 2010-2014.
29. H. Binder Cox models by likelihood based boosting for a single survival endpoint or competing risks. R package version 1.2-1, 2010, .
30. L. J. van't Veer, H. Dai, M. J. van de Vijver, D. Yudong, A. A. M. Hart, M. Mao, H. L. Peterse, K. van der Kooy, M. J. Marton, A. T. Witteveen, G. J. Schreiber, R. M. Kerkhoven, C. Roberts, P. S. Linsley, R. Bernards, and S. H. Friend, Gene expression profiling predicts clinical outcome of breast cancer, Nature 415 (2002), 530-536.
31. M. J. van de Vijver, Y. D. He, L. J. van't Veer, H. Dai, A. A. M. Hart, D. W. Voskuil, G. J. Schreiber, J. L. Peterse, C. Roberts, M. J. Marton, M. Parrish, D. Atsma, A. Witteveen, A. Glas, L. Delahaye, T. van der Velde, H. Bartelink, S. Rodenhuis, E. T. Rutgers, S. H. Friend, and R. Bernards, A gene-expression signature as a predictor of survival in breast cancer, N Engl J Med 347 (2002), 1999-2009.
32. H. Ishwaran, E. H. Blackstone, C. A. Hansen, and T. W. Rice, A novel approach to cancer staging: application to esophageal cancer, Biostatistics 10 (2009), 603-620.