Tree-Based Methods for Survival Data

Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics

Volumn , Issue , 2007, Pages 265-285

  Banerjee, Mousumi ^a   Noone, Anne Michelle ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Classification and regression of trees (CART) review; Tree based methods and tree based survival analyses; Within node error between node separation measure

Indexed keywords

SEPARATION MEASURES; SURVIVAL DATA; TREE-BASED; TREE-BASED METHODS;

TREES (MATHEMATICS);

EID: 84954602489     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470181218.ch16     Document Type: Chapter

References (46)

1. Albain, K. S., Crowley, J. J., LeBlanc, M., and Livingston, R. B., Determinants of improved outcome in small-cell lung cancer: An analysis of the 2,580-patient Southwest Oncology Group data base, J. Clin. Oncol. 8, 1563-1574 (1990).
2. Albain, K. S., Green, S., LeBlanc, M., Rivkin, S., O'Sullivan, J., and Osborne, C. K., Proportional hazards and recursive partitioning and amalgamation analyses of the Southwest Oncology Group node-positive adjuvant CMFVP breast cancer data base: A pilot study, Breast Cancer Res. Treat. 22, 273-284 (1992).
3. Banerjee, M., Biswas, D., Sakr, W., and Wood, D. P., Jr., Recursive partitioning for prognostic grouping of patients with clinically localized prostate carcinoma, Cancer 89, 404-411 (2000).
4. Banerjee, M., George, J., Song, E. Y., Roy, A., and Hryniuk, W., Tree-based model for breast cancer prognostication, J. Clin. Oncol. 22, 2567-2575 (2004).
5. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J., Classification and Regression Trees, Wadsworth, Belmont, CA, 1984.
6. Breiman, L., Bagging predictors, Machine Learn. 24, 123-140 (1996).
7. Breiman, L., Random forests, Machine Learn. 45, 5-32 (2001).
8. Breiman, L., How to Use Survival Forests, http://www.stat.berkeley.edu/users/breiman/, 2002.
9. Breslow, N., Contribution to the discussion of the paper by D. R. Cox, J. Roy. Statist. Soc. Ser. B 34, 216-217 (1972).
10. Ciampi, A., Chang, C.-H., Hogg, S., and McKinney, S., Recursive partitioning: A versatile method for exploratory data analysis in biostatistics, in MacNeil, I. B. and Umphrey, G. J., eds., Biostatistics, Reidel, Dordrecht, 1987, pp. 23-50.
11. Ciampi, A., Hogg, S., McKinney, S., and Thiffault, J., RECPAM: A computer program for recursive partitioning and amalgamation for censored survival data, Comput. Meth. Programs Biomed. 26, 239-256 (1988).
12. Davis, R. B. and Anderson, J. R., Exponential survival trees, Statist. Med. 8, 947-961 (1989).
13. Freedman, G. M., Hanlon, A. L., Fowble, B. L., Anderson, P. R., and Nicoloau, N., Recursive partitioning identifies patients at high and low risk for ipsilateral tumor recurrence after breast-conserving surgery and radiation, J. Clin. Oncol. 20, 4015-4021 (2002).
14. Freund, Y. and Schapire, R. E., Experiments with a new boosting algorithm, in Proc. 13th Int. Conf. Machine Learning, Bari, Italy, 1996, pp. 148-156.
15. Goldman, L., Cook, F., Johnson, P., Brand, D., Rouan, G., and Lee, T., Prediction of the need for intensive care in patients who come to emergency departments with acute chest pain, New Engl. J. Med. 334, 1498-1504 (1996).
16. Gordon, L. and Olshen, R., Tree-structured survival analysis, Cancer Treat. Rep. 69, 1065-1069 (1985).
17. Grambsch, P. M., Therneau, T. M., and Fleming, T. R., Diagnostic plots to reveal functional form for covariates in multiplicative intensity models, Biometrics 51, 1469-1482 (1995).
18. Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning, Springer, New York, 2001.
19. Hothorn, T., Lausen, B., Benner, A., and Radespiel-Tröger, M., Bagging survival trees, Statist. Med. 23, 77-94 (2004).
20. Hothorn, T., Buhlmann, P., Dudoit, S., Molinaro, A., and van der Laan, M. J., Survival ensembles, Biostatistics 7, 355-373 (2006).
21. Intrator, O. and Kooperberg, C., Trees and splines in survival analysis, Statist. Meth. Med. Res. 4, 237-261 (1995).
22. Ishwaran, H., Blackstone, E. H., Pothier, C. E., and Lauer, M. S., Relative risk forests for exercise heart rate recovery as a predictor of mortality, J. Am. Statist. Assoc. 99, 591-600 (2004).
23. Katz, A., Buchholz, T. A., Thames, H., Smith, C. D., McNeese, M. D., Theriault, R., Singletary, S. E., and Strom, E. A., Recursive partitioning analysis of locoregional recurrence patterns following mastectomy: Implications for adjuvant irradiation, Int. J. Radiat. Oncol. Biol. Phys. 50, 397-403 (2001).
24. Keles, S. and Segal, M. R., Residual-based tree-structured survival analysis, Statist. Med. 21, 313-326 (2002).
25. LeBlanc, M., Tree-based methods for prognostic stratification, in Handbook of Statistics in Clinical Oncology, Crowley, J., ed., Marcel Dekker, New York, 2001, pp. 457-472.
26. LeBlanc, M. and Crowley, J., Relative risk trees for censored survival data, Biometrics 48, 411-425 (1992).
27. LeBlanc, M. and Crowley, J., Survival trees by goodness of split, J. Am. Statist. Assoc. 88, 457-467 (1993).
28. LeBlanc, M. and Crowley, J., Adaptive regression splines in the Cox model, Biometrics 55, 204-213 (1999).
29. Liaw, A. and Wiener, M., Classification and regression by random-Forest, R News 2, 18-22 (2002).
30. Morgan, J. N. and Messenger, R. C., THAID: A Sequential Search Program for the Analysis of Nominal Scale Dependent Variables, Technical Report, Institute for Social Research, Univ. Michigan, Ann Arbor, 1973.
31. Morgan, J. N. and Sonquist, J. A., Problems in the analysis of survey data and a proposal, J. Am. Statist. Assoc. 58, 415-434 (1963).
32. Nelson, W., On estimating the distribution of a random vector when only the coordinate is observable, Technometrics 12, 923-924 (1969).
33. Quinlan, J., Bagging, boosting, and C4.5, in Proc. 13th American Association for Artificial Intelligence National Conf. Artificial Intelligence, Menlo Park, CA, AAAI Press, 1996, pp. 725-730.
34. R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2005 (ISBN 3-900051-07-0, URL http://www.R-project.org).
35. Segal, M., Regression trees for censored data, Biometrics 44, 35-48 (1988).
36. Segal, M., Extending the elements of tree-structured regression, Statist. Meth. Med. Res. 4, 219-236 (1995).
37. Segal, M., James, I. R., French, M. A. H., and Mallal, S., Statistical issues in the evaluation of markers of HIV progression, Int. Statist. Rev. 63, 179-197 (1995).
38. Segal, M., Barbour, J. D., and Grant, R. M., Relating HIV-1 sequence variation to replication capacity via trees and forests, Statist. Appl. Genet. Molec. Biol. 3(1) Art. 2 (2004).
39. Segal, M., Microarray gene expression data with linked survival phenotypes: diffuse large B-cell lymphoma revisited, Biostatistics 7, 268-285 (2006).
40. Therneau, T. M., Grambsch, P. M., and Fleming, T. R., Martingale-based residuals for survival models, Biometrika 77, 147-160 (1990).
41. Therneau, T. M. and Grambsch, P. M., Modeling Survival Data: Extending the Cox Model, Springer, New York, 2000.
42. Therneau, T. M. and Atkinson, B., R port by Brian Ripley kripley@stats.ox.ac.ukl. rpart: Recursive Partitioning. R package version 3.1-27, 2005.
43. Zhang, H., Splitting criteria in survival trees, in Statistical Modelling, Proc. 10th Int. Workshop on Statistical Modelling, Innsbruck, Austria, July 1995, Seeber, G. U. H., Francis, B. J., Hatzinger, R., and Steckel-Berger, G., eds., Springer, New York, 1995, pp. 305-314.
44. Zhang, H. and Singer, B., Recursive Partitioning in the Health Sciences, Springer, New York, 1999.
45. Zhang, H., Yu, C. Y., Singer, B., and Xiong, M. M., Recursive partitioning for tumor classification with gene expression microarray data, Proc. Natl. Acad. Sci. USA 98, 6730-6735 (2001).
46. Zhang, H., Yu, C. Y., and Singer, B., Cell and tumor classification using gene expression data: Construction of forests, Proc. Natl. Acad. Sci. USA 100, 4168-4172 (2003).