Boosting the concordance index for survival data - A unified framework to derive and evaluate biomarker combinations

PLoS ONE

Volumn 9, Issue 1, 2014, Pages

  Mayr, Andreas ^a   Schmid, Matthias ^b  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER;

ALGORITHM; ARTICLE; BIOINFORMATICS; BIOSTATISTICS; BREAST CANCER; CANCER PATIENT; CONCORDANCE INDEX; HUMAN; METHODOLOGY; NONPARAMETRIC TEST; OUTCOME ASSESSMENT; STATISTICAL PARAMETERS; SURVIVAL PREDICTION;

ALGORITHMS; BIOLOGICAL MARKERS; BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; FEMALE; GENE EXPRESSION PROFILING; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NEOPLASM METASTASIS; PROGNOSIS; SURVIVAL ANALYSIS;

EID: 84896968781     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0084483     Document Type: Article

References (51)

1. Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, et al. (2007) Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independentvalidation series. Clinical Cancer Research 13: 3207-3214. (Pubitemid 46944903)
2. van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AAM, et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. New England Journal of Medicine 347: 1999-2009. (Pubitemid 35461656)
3. Kok M, Linn SC, Laar RKV, Jansen MPHM, van den Berg TM, et al. (2009) Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen. Breast Cancer Research and Treatment 13: 275-283.
4. Li H, Gui J (2004) Partial Cox regression analysis for high-dimensional microarray gene expression data. Bioinformatics 20: 208-215.
5. Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, et al. (2004) Gene expression signature of fibroblast serum response predicts human cancer progression: Similarities between tumors and wounds. PLoS Biology 2.
6. Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, et al. (2008) Serum micrornas are promising novel biomarkers. PLoS ONE 3: e3148.
7. Wistuba II, Gelovani JG, Jacoby JJ, Davis SE, Herbst RS (2011) Methodological and practical challenges for personalized cancer therapies. Nature Reviews Clinical Oncology 8: 135-141.
8. Witten DM, Tibshirani R (2010) Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 19: 29-51.
9. Ma S, Song X (2011) Ranking prognosis markers in cancer genomic studies. Brie-ngs in Bioinformatics 12: 33-40.
10. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, et al. (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365: 671-679. (Pubitemid 40260888)
11. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Statistics in Medicine 16: 385-395.
12. 1 penalized estimation in the Cox proportional hazards model. Biometrical Journal 52: 70-84.
13. Li H, Luan Y (2002) Kernel cox regression models for linking gene expression profiles to censored survival data. In: Pacific Symposium on Biocomputing. volume 8, p. 65.
14. Gui J, Li H (2005) Penalized cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 21: 3001-3008. (Pubitemid 40916425)
15. Schmid M, Potapov S (2012) A comparison of estimators to evaluate the discriminatory power of time-to-event models. Statistics in Medicine 31: 2588-2609.
16. Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA (1982) Evaluating the yield of medical tests. Journal of the American Medical Association 247: 2543-2546. (Pubitemid 12155954)
17. Harrell FE, Lee KL, Califf, R M (1984) Regression modeling strategies for improved prognostic prediction. Statistics in Medicine 3: 143-152. (Pubitemid 14052973)
18. Uno H, Cai T, Pencina MJ, D'Agostino RB, Wei LJ (2011) On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 30: 1105-1117.
19. Kaderali L, Zander T, Faigle U, Wolf J, Schultze JL, et al. (2006) CASPAR: A hierarchical bayesian approach to predict survival times in cancer from gene expression data. Bioinformatics 22: 1495-1502. (Pubitemid 43985321)
20. Pepe MS, Zheng Y, Jin Y (2008) Evaluating the ROC performance of markers for future events. Lifetime Data Analysis 14: 86-113.
21. Zhang H, Xia W, Lu X, Sun R, Wang L, et al. (2013) A novel statistical prognostic score model that includes serum CXCL5 levels and clinical classification predicts risk of disease progression and survival of nasopharyngeal carcinoma patients. PLOS ONE 8: e57830.
22. Zhao X, Rdland EA, Srlie T, Naume B, Langerd A, et al. (2011) Combining gene signatures improves prediction of breast cancer survival. PLoS ONE 6: e17845.
23. Friedman JH (1997) On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery 1: 55-77.
24. B?uhlmann P, Hothorn T (2007) Boosting algorithms: Regularization, prediction and model fitting (with discussion). Statistical Science 22: 477-522.
25. Harrell F, Lee KL, Mark DB (1996) Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15: 361-387. (Pubitemid 26072233)
26. Heagerty PJ, Zheng Y (2005) Survival model predictive accuracy and ROC curves. Biometrics 61: 92-105. (Pubitemid 40380968)
27. PubMed (2013). US national library of medicine national institutes of health. URL http://www.ncbi.nlm.nih.gov/pubmed/.
28. Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, et al. (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. Journal of the National Cancer Institute 81: 1879-1886. (Pubitemid 20026073)
29. Tice J, Cummings S, Ziv E, Kerlikowske K (2005) Mammographic breast density and the gail model for breast cancer risk prediction in a screening population. Breast Cancer Research and Treatment 94: 115-122. (Pubitemid 41547108)
30. Antolini L, Boracchi P, Biganzoli E (2005) A time-dependent discrimination index for survival data. Statistics in Medicine 24: 3927-3944. (Pubitemid 43115739)
31. van der Laan MJ, Robins JM (2003) Unified Methods for Censored Longitudinal Data and Causality. New York: Springer.
32. Schmid M, Kestler HA, Potapov S (2013) On the validity of time-dependent AUC estimators. Briefings in Bioinformatics.
33. Friedman JH, Hastie T, Tibshirani R (2000) Additive logistic regression: A statistical view of boosting (with discussion). The Annals of Statistics 28: 337-407. (Pubitemid 33227445)
34. Ma S, Huang J (2005) Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics 21: 4356-4362. (Pubitemid 41782925)
35. Buehlmann P (2006) Boosting for high-dimensional linear models. The Annals of Statistics : 559-583.
36. Schmid M, Hothorn T (2008) Boosting additive models using component-wise P-splines. Computational Statistics & Data Analysis 53: 298-311.
37. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B (2013) mboost: Model-Based Boosting. URL http://CRAN.R-project.org/package=mboost. R package version 2.2-3.
38. Mayr A, Hofner B, Schmid M (2012) The importance of knowing when to stop. A sequential stopping rule for component-wise gradient boosting. Methods of Information in Medicine 51: 178-186.
39. R Core Team (2013) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. ISBN 3-900051-07-0.
40. Lecocke M, Hess K (2006) An empirical study of univariate and genetic algorithm-based feature selection in binary classification with microarray data. Cancer Informatics 2: 313-327. (Pubitemid 44691090)
41. Klein JP, Moeschberger ML (2003) Survival Analysis - Techniques for Censored and Truncated Data. New York: Springer, 2nd edition.
42. Mayr A, Fenske N, Hofner B, Kneib T, Schmid M (2012) Generalized additive models for location, scale and shape for high-dimensional data - a exible approach based on boosting. Journal of the Royal Statistical Society: Series C (Applied Statistics) 61: 403-427.
43. Goeman J (2012) penalized: L1 (Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model. R package version 0.9-42.
44. Potapov S, Adler W, Schmid M (2012) survAUC: Estimators of Prediction Accuracy for Time-to-Event Data. R package version 1.0-5. http://cran.rproject. org/web/packages/survAUC/index.html.
45. Hothorn T, B?uhlmann P, Dudoit S, Molinaro A, Van Der Laan M (2006) Survival ensembles. Biostatistics 7: 355-373.
46. Schmid M, Hothorn T (2008) Flexible boosting of accelerated failure time models. BMC Bioinformatics 9: 269.
47. van't Veer LJ, Dai HY, van de Vijver MJ, He YDD, Hart AAM, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530-536. (Pubitemid 34130608)
48. van Houwelingen HC, Bruinsma T, Hart AA, van't Veer LJ, Wessels LF (2006) Cross-validated cox regression on microarray gene expression data. Statistics in Medicine 25: 3201-3216. (Pubitemid 44377341)
49. Pencina MJ, D'Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: Model speci-c population value and confidence interval estimation. Statistics in Medicine 23: 2109-2123.
50. Wang Z, Chang Y (2011) Marker selection via maximizing the partial area under the ROC curve of linear risk scores. Biostatistics 12: 369-385.
51. Schmid M, Hothorn T, Krause F, Rabe C (2012) A PAUC-based estimation technique for disease classification and biomarker selection. Statistical Applications in Genetics and Molecular Biology 11, Article 3.