Super Learner for Survival Data Prediction

References

[1] Cox DR. Regression models and life-tables. J R Stat Soc. Ser B. 1972;34:187–220.10.1007/978-1-4612-4380-9_37Search in Google Scholar

[2] Tibshirani R. The lasso method for variable selection in the cox model. Stat Med. 1997;16:385–95.10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3Search in Google Scholar

[3] Verweij PJ, van Houwelingen HC. Penalized likelihood in cox regression. Stat Med. 199413:2427–36.10.1002/sim.4780132307Search in Google Scholar

[4] Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for cox’s proportional hazards model via coordinate descent. J Stat Softw 2011;39:1.10.18637/jss.v039.i05Search in Google Scholar

[5] Schapire RE. The strength of weak learnability. Mach Learn. 1990;5:197–227.10.1109/SFCS.1989.63451Search in Google Scholar

[6] Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat. 2000;28:337–407.10.1214/aos/1016218223Search in Google Scholar

[7] Bühlmann P, Yu B. Boosting with the l2 loss: regression and classification. J Am Stat Assoc 2003;98:324–39.10.1198/016214503000125Search in Google Scholar

[8] Tutz G, Binder H. Generalized additive modeling with implicit variable selection by likelihood-based boosting. Biometrics 2006;62:961–71.10.1111/j.1541-0420.2006.00578.xSearch in Google Scholar

[9] De Bin R. Boosting in cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the r-packages coxboost and mboost. Comput Stat 2016;31:513–31.10.1007/s00180-015-0642-2Search in Google Scholar

[10] Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat 2008;2:841–60.10.1002/9781118445112.stat08188Search in Google Scholar

[11] Breiman L. Random forests. Mach Learn. 2001;45:5–32.10.1023/A:1010933404324Search in Google Scholar

[12] Nelson W. Theory and applications of hazard plotting for censored failure data. Technometrics 1972;14:945–66.10.1080/00401706.1972.10488991Search in Google Scholar

[13] Aalen O. Nonparametric inference for a family of counting processes. Ann Stat. 1978;6:701–726.10.1214/aos/1176344247Search in Google Scholar

[14] van der Laan MJ, Polley EC, Hubbard AE. ‘Super learner. Stat Appl Genet Mol Biol. 2007;6:1–23.10.2202/1544-6115.1309Search in Google Scholar

[15] Wolpert DH. Stacked generalization. Neural networks 1992;5:241–59.10.1016/S0893-6080(05)80023-1Search in Google Scholar

[16] Breiman L. Stacked regressions. Mach Learn. 1996;24:49–64.10.1007/BF00117832Search in Google Scholar

[17] van der Laan MJ, Dudoit S. Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: finite sample oracle inequalities and examples, Uc berkeley division of biostatistics working papers series, paper 130, U.C. Berkeley, 2003. https://biostats.bepress.com/ucbbiostat/paper130.Search in Google Scholar

[18] van der Vaart A, Dudoit S, van der Laan MJ. Oracle inequalities for multi-fold cross validation. Stat Decisions. 2006;24:351–71.10.1524/stnd.2006.24.3.351Search in Google Scholar

[19] Polley EC, Rose S, van der Laan MJ. Super learner in prediction. In MJ van der Laan, S Rose, editors. Targeted learning: causal inference for observational and experimental data. New York: Springer, 2011.10.1007/978-1-4419-9782-1Search in Google Scholar

[20] Polley EC, van der Laan MJ. Super learning for right-censored data. New York, NY: Springer New York, 2011.10.1007/978-1-4419-9782-1_16Search in Google Scholar

[21] Wey A, Connett J, Rudser K. Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models. Biostatistics 2015;16:537–49.10.1093/biostatistics/kxv001Search in Google Scholar

[22] Hastie TJ, Tibshirani RJ. Generalized additive models, monographs on statistics and applied probability. London: Chapman & Hall, CRC, 1990.Search in Google Scholar

[23] Lorbert A, Ramadge P. Descent methods for tuning parameter refinement. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010:469–476.Search in Google Scholar

[24] Breslow NE. Contribution to the discussion of paper by d.r. cox. J R Stat Soc. Ser B. 1972;34:216–7.Search in Google Scholar

[25] Therneau TM, Lumley T. survival: R package version 2.42, 2018. https://CRAN.R-project.org/package=survival.Search in Google Scholar

[26] Binder H. CoxBoost: cox models by likelihood based boosting for a single survival endpoint or competing risks, R package version 1.0, 2013. https://CRAN.R-project.org/package=CoxBoost.Search in Google Scholar

[27] Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B. mboost: Model-based boosting, R package version 2.5-0, 2015. https://CRAN.R-project.org/package=mboost.Search in Google Scholar

[28] Ridgeway G. gbm: Generalized boosted regression models. R package version 1.6-3, 2007. https://CRAN.R-project.org/package=gbm.Search in Google Scholar

[29] Ishwaran H, Kogalur UB. randomForestSRC. R package version 2.7, 2018. https://CRAN.R-project.org/package=randomForestSRC.Search in Google Scholar

[30] Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87.10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4Search in Google Scholar

[31] Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. Jama 1982;247:2543–6.10.1001/jama.1982.03320430047030Search in Google Scholar

[32] Loprinzi CL, Laurie JA, Wieand HS, Krook JE, Novotny PJ, Kugler JW, et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. north central cancer treatment group. J Clin Oncol 1994;12:601–7.10.1200/JCO.1994.12.3.601Search in Google Scholar PubMed

[33] Mantel N, Bohidar NR, Ciminera JL. Mantel-haenszel analyses of litter-matched time-to-response data, with modifications for recovery of interlitter information. Cancer Res. 1977;37:3863–8.Search in Google Scholar

[34] Laurie JA, Moertel C, Fleming TR, Wieand HS, Leigh JE, Rubin J et al. Surgical adjuvant therapy of large-bowel carcinoma: an evaluation of levamisole and the combination of levamisole and fluorouracil. the north central cancer treatment group and the mayo clinic. J Clin Oncol. 1989;7:1447–56.10.1200/JCO.1989.7.10.1447Search in Google Scholar PubMed

[35] Lin DY. Cox regression analysis of multivariate failure time data: the marginal approach. Stat Med 1994;13:2233–47.10.1002/sim.4780132105Search in Google Scholar PubMed

[36] Moertel CG, Fleming TR, Macdonald JS, Haller DG, Laurie JA, Goodman PJ, et al. Levamisole and fluorouracil for adjuvant therapy of resected colon carcinoma. N Engl J Med. 1990;322:352–8.10.1056/NEJM199002083220602Search in Google Scholar PubMed

[37] Moertel CG, Fleming TR, Macdonald JS, Haller DG, Laurie JA, Tangen CM, et al. Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage iii colon carcinoma: a final report. Ann Int Med. 1995;122:321–6.10.7326/0003-4819-122-5-199503010-00001Search in Google Scholar PubMed

[38] Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. New York: Wiley, 1980.Search in Google Scholar

[39] McGilchrist CA, Aisbett CW. Regression with frailty in survival analysis. Biometrics, 1991;47:461–6.10.2307/2532138Search in Google Scholar

[40] Huster WJ, Brookmeyer R, Self SG. Modelling paired survival data with covariates. Biometrics. 1989;45:145–56.10.2307/2532041Search in Google Scholar

[41] Blair AL, Hadden DR, Weaver JA, Archer DB, Johnston PB, Maguire CJ. The 5-year prognosis for vision in diabetes. Ulster Med J. 1980;49:139.Search in Google Scholar