The benefit of data-based model complexity selection via prediction error curves in time-to-event data

Computational Statistics

Volumn 26, Issue 2, 2011, Pages 293-302

  Porzelius, Christine ^a,b   Schumacher, Martin ^a,b   Binder, Harald ^a,b  

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Model complexity; Model selection; Prediction error curves; Random survival forests; Time to event data

Indexed keywords

EID: 79955467766     PISSN: 09434062     EISSN: 16139658     Source Type: Journal    
DOI: 10.1007/s00180-011-0236-6     Document Type: Article

References (31)

1. Binder H, Schumacher M (2008) Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples. Stat Appl Genet Mol Biol. 7(1): 12. http://www. bepress. com/sagmb/vol7/iss1/art12.
2. Binder H, Schumacher M (2008) Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinform 9: 14.
3. Breiman L (2001) Random forests. Mach Learn 45: 5-32.
4. Breiman L (2002) Manual on setting up, using, and understanding random forests v3. 1. http://oz. berkeley. edu/users/breiman/Using_random_forests_V3. 1. pdf.
5. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Wadsworth & Brooks, Monterey.
6. Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78: 1-3.
7. Cox DR (1972) Regression models and life-tables (with discussion). J Roy Stat Soc Ser B Methodol 34: 187-220.
8. Efron B, Tibshirani R (1997) Improvements on cross-validation: the. 632+ bootstrap method. J Am Stat Assoc 92(438): 548-560.
9. Gerds TA, Cai T, Schumacher M (2008) The performance of risk prediction models. Biom J 50(4): 457-479.
10. Gerds TA, Schumacher M (2006) Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biom J 48: 1029-1040.
11. Gerds TA, Schumacher M (2007) Efron-type measures of prediction error for survival analysis. Biometrics 63(4): 1283-1287. doi: 10. 1111/j. 1541-0420. 2007. 00832. x.
12. Gneiting T, Raftery A (2007) Strictly proper scoring rules, prediction and estimation. J Am Stat Assoc 102(477): 359-378.
13. Graf E, Schmoor C, Sauerbrei W, Schumacher M (1999) Assessment and comparison of prognostic classification schemes for survival data. Stat Med 18: 2529-2545.
14. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York.
15. Ishwaran H, Kogalur UB (2008) randomSurvivalForest: Ishwaran and Kogalur's random survival forest. http://www. bio. ri. ccf. org/Resume/Pages/Ishwaran/ishwaran. html, http://www. kogalur-shear. com, R package version 3. 5. 1.
16. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008) Random survival forests. Ann Appl Stat 2(3): 841-860.
17. Ishwaran H, Kogalur UB, Gorodeski EZ, Minn AJ, Lauer MS (2010) High-dimensional variable selection for survival data. J Am Stat Assoc 105(489): 205-217.
18. Knaus J, Porzelius C, Binder H, Schwarzer G (2009) Easier parallel computing in R with snowfall and sfCluster. R J 1: 54-59.
19. Oberthür A, Kaderali L, Kahlert Y, Hero B, Westermann F, Berthold F, Brors B, Eils R, Fischer M (2008) Subclassification and individual survival time prediction from gene expression data of neuroblastoma patients by using CASPAR. Clin Cancer Res 14(20): 6590-6601.
20. Porzelius C, Binder H (2009) peperr: Parallelised estimation of prediction error. http://cran. r-project. org, R package version 1. 1-4.
21. Porzelius C, Binder H, Schumacher M. (2009) Parallelized prediction error estimation for evaluation of high-dimensional models. Bioinformatics 25(6): 827-829 http://bioinformatics. oxfordjournals. org/cgi/content/abstract/btp062v1, doi: 10. 1093/bioinformatics/btp062.
22. Porzelius C, Schumacher M, Binder H (2010) A general, prediction error based criterion for selecting model complexity for high-dimensional survival models. Stat Med 29: 830-838.
23. R Development Core Team (2009) R: A language and environment for statistical computing. Vienna, Austria http://www. R-project. org, ISBN 3-900051-07-0.
24. Radespiel-Tröger M, Gefeller O, Rabenstein T, Hothorn T (2006) Association between split selection instability and predictive error in survival trees. Methods Inf Med 45(5): 548-556.
25. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyna RD, Muller-Hermelink HK, Smeland EB, Staudt LM (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New England J Med 346(25): 1937-1946.
26. Schumacher M, Binder H, Gerds TA (2007) Assessment of survival prediction models based on microarray data. Bioinformatics 23(14): 1768-1774.
27. Simon R, Radmacher M, Dobbin K, McShane L (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95(1): 14-18.
28. Strobl C, Boulesteix AL, Kneib T, Augustin T, Hothorn T (2008) Conditional variable importance for random forests. BMC Bioinform 9(1): 307.
29. Strobl C, Boulesteix AL, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform 8(1): 25.
30. Zhu M (2008) Kernels and ensembles: perspectives on statistical learning. Am Stat 62(2): 97-109.
31. Ziegler A, König IR, Thompson JR (2008) Biostatistical aspects of genome-wide association studies. Biometr J 50(1): 8-28.