A general, prediction error-based criterion for selecting model complexity for high-dimensional survival models

Statistics in Medicine

Volumn 29, Issue 7-8, 2010, Pages 830-838

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

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Bootstrap; High dimensional survival data; Model complexity; Model selection; Prediction error curves

Indexed keywords

ANALYTICAL ERROR; ARTICLE; BOOTSTRAPPING; LARGE CELL LYMPHOMA; MICROARRAY ANALYSIS; PREDICTION; SIMULATION; STATISTICAL MODEL; STATISTICAL PARAMETERS; SURVIVAL; VALIDATION PROCESS;

BIAS (EPIDEMIOLOGY); BIOSTATISTICS; COMPUTER SIMULATION; HUMANS; LIKELIHOOD FUNCTIONS; LYMPHOMA, LARGE B-CELL, DIFFUSE; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; SURVIVAL ANALYSIS; TUMOR MARKERS, BIOLOGICAL;

EID: 77949414926     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3765     Document Type: Article

References (23)

1. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B (Methodological) 1996; 58(1):267-288.
2. Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine 1997; 16(4):385-395.
3. Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 2001; 96(456):1348-1360.
4. Fan J, Li R. Variable selection for Cox's proportional hazards model and frailty model. Annals of Statistics 2002; 30(1):74-99.
5. Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B 2005; 67(2):301-320.
6. Candes E, Tao T. The Dantzig selector: statistical estimation when p is much larger than n. Annals of Statistics 2007; 35(6):2313-2351.
7. Boulesteix AL, Strimmer K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Briefings in Bioinformatics 2007; 8(1):32-44.
8. Fan J, Lv J. Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society, Series B - Statistical Methodology 2008; 70(Part 5):849-883.
9. Binder H, Schumacher M. Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics 2008; 9:14.
10. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Annals of Applied Statistics 2008; 2(3):841-860.
11. Bøvelstad HM, Nygård S, Størvold HL, Aldrin M, Borgan Ø, Frigessi A, Lingjærde OC. Predicting survival from microarray data - a comparative study. Bioinformatics 2007; 23(16):2080-2087.
12. Verweij PJM, van Houwelingen HC. Cross-validation in survival analysis. Statistics in Medicine 1993; 12(24):2305-2314.
13. Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine 1999; 18:2529-2545.
14. Efron B, Tibshirani R. Improvements on cross-validation: the .632+ bootstrap method. Journal of the American Statistical Association 1997; 92(438):548-560.
15. Gerds TA, Schumacher M. Efron-type measures of prediction error for survival analysis. Biometrics 2007; 63(4):1283-1287. Available from: http://dx.doi.org/10.1111/j.1541-0420.2007.00832.x.
16. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyna RD, Muller-Hermelink HK, Smeland EB, Staudt LM. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. The New England Journal of Medicine 2002; 346(25):1937-1946.
17. van Houwelingen HC, Bruinsma T, Hart AAN, van 't Veer L, Wessels L. Cross-validated Cox regression on microarray gene expression data. Statistics in Medicine 2006; 25:3201-3216.
18. Gerds TA, Schumacher M. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical Journal 2006; 48:1029-1040.
19. Binder H, Schumacher M. Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples. Statistical Applications in Genetics and Molecular Biology 2008; 7(1):12. Available from: URL http://www.bepress.com/sagmb/vol7/iss1/art12.
20. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine 2005; 24:1713-1723.
21. R Development Core Team. R: A Language and Environment for Statistical Computing, Vienna, Austria, 2008. Available from: http://www.R-project. org, ISBN 3-900051-07-0.
22. Breiman L. Manual on Setting Up, Using, and Understanding Random Forests v3.1, 2002. Available from: http://oz.berkeley.edu/users/breiman/ Using-random-forests-V3.1.pdf.
23. Porzelius C, Binder H, Schumacher M. Parallelized prediction error estimation for evaluation of high-dimensional models. Bioinformatics 2009; 25(6):827-829. DOI: 10.1093/bioinformatics/btp062. Available from: http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btp062v1.