On the prognostic value of survival models with application to gene expression signatures

Statistics in Medicine

Volumn 29, Issue 7-8, 2010, Pages 818-829

  Hielscher, T ^a   Zucknick, M ^a   Werft, W ^a   Benner, A ^a  

^a GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

Author keywords

Censored data; High dimensional; R2 coefficient

Indexed keywords

ANALYSIS OF VARIANCE; ANALYTICAL ERROR; ARTICLE; COMPUTER PROGRAM; COVARIANCE; GENE EXPRESSION; LINEAR REGRESSION ANALYSIS; MICROARRAY ANALYSIS; PREDICTIVE VALIDITY; PROGNOSIS; SIMULATION; STATISTICAL MODEL; STATISTICAL PARAMETERS; SURVIVAL; SURVIVAL TIME;

BIOSTATISTICS; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; HUMANS; LYMPHOMA, LARGE B-CELL, DIFFUSE; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROGNOSIS; SOFTWARE; SURVIVAL ANALYSIS;

EID: 77949347679     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3768     Document Type: Article

References (34)

1. Verweij P, van Houwelingen H. Crossvalidation in survival analysis. Statistics in Medicine 1993; 12:2305-2314.
2. 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.
3. Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Statistics in Medicine 2004; 23:723-748.
4. Verweij P, van Houwelingen H. Penalized likelihood in Cox regression. Statistics in Medicine 1994; 13:2427-2436.
5. 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.
6. Schemper M, Henderson R. Predictive accuracy and explained variation in Cox regression. Biometrics 2000; 56:249-255.
7. Korn EL, Simon R. Measures of explained variation for survival data. Statistics in Medicine 1990; 9:487-503.
8. Brier GW. Verification of forecasts expressed in terms of probability. Monthly Weather Review 1950; 78:1-3.
9. Kent JT, O'Quigley J. Measures of dependence for censored survival data. Biometrika 1988; 75:525-534.
10. Cox DR, Snell EJ. The Analysis of Binary Data (2nd edn). Chapman & Hall: London, 1989.
11. 2 measures based on Wald and likelihood ratio joint significance tests. American Statistician 1990; 44:250-253.
12. Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika 1991; 78:691-692.
13. O'Quigley J, Xu R, Stare J. Explained randomness in proportional hazards models. Statistics in Medicine 2005; 24:479-489.
14. Royston P. Explained variation for survival models. The Stata Journal 2006; 6:83-96.
15. Kullback S, Leibler RA. On information and sufficiency. Annals of Mathematical Statistics 1951; 22:79-86.
16. Xu R, O'Quigley J. A measure of dependence for proportional hazards models. Journal of Nonparametric Statistics 1999; 12:83-107.
17. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika 1982; 69:239-241.
18. Heagerty P, Zheng Y. Survival model prediction accuracy and ROC curves. Biometrics 2005; 61:92-105.
19. Pepe M, Zheng Y, Jin Y, Ying H, Parikh C, Levy W. Evaluating the ROC performance of markers for future events. Lifetime Data Analysis 2007; 14:86-113.
20. Harrell F, Lee K, Mark D. Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 1996; 15:287-361.
21. Pencina M, D'Agostina Sr R, D'Agostina Jr R, Vasan R. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Biometrics 2008; 61:92-105.
22. Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine 2005; 24:1713-1723.
23. Lin L. A note on the concordance correlation coefficient. Biometrics 2000; 56:324-325.
24. Suzuki R, Shimodaira H. pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 2006; 22:1540-1542.
25. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 1996; 58:267-288.
26. Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine 1997; 16:385-395.
27. Goeman J. Penalized: L1 (lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model. R package version 0.9-22, 2008. Available from: http://www.msbi.nl/goeman.
28. Goeman J. L1 penalized estimation in the Cox proportional hazards model. Biometrical Journal 2009. Available from: http://www.msbi.nl/goeman; in press.
29. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland EB, Giltnane JM, Hurt EM, Zhao H, Averett L, Yang L, Wilson WH, Jaffe ES, Simon R, Klausner RD, Powell J, Duffey PL, Longo DL, Greiner TC, Weisenburger DD, Sanger WG, Dave BJ, Lynch JC, Vose J, Armitage JO, Montserrat E, López-Guillermo A, Grogan TM, Miller TP, LeBlanc M, Ott G, Kvaloy S, Delabie J, Holte H, Krajci P, Stokke T, Staudt LM, for the Lymphoma/Leukemia Molecular Profiling Project. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New England Journal of Medicine 2002; 346:1937-1947.
30. Segal MR. Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited. Biostatistics 2006; 7(2):268-285.
31. Schumacher M, Binder H, Gerds T. Assessment of survival prediction models based on microarray data. Bioinformatics 2007; 23:1768-1774.
32. Dunkler D, Michiels S, Schemper M. Gene expression profiling: does it add predictive accuracy to clinical characteristics in cancer prognosis? European Journal of Cancer 2007; 43:745-751.
33. 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; 7:12.
34. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria 2007. Available from: http://www.R-project.org. ISBN 3-900051-07-0