References

Aalen, Odd. 1978. “Nonparametric Inference for a Family of Counting Processes.” The Annals of Statistics 6 (4): 701–26.

Aalen, Odd, Ornulf Borgan, and Hakon Gjessing. 2008. Survival and Event History Analysis: A Process Point of View. 2008th ed. Springer.

Aalen, Odd, and Søren Johansen. 1978. “An Empirical Transition Matrix for Non-Homogeneous Markov Chains Based on Censored Observations.” Scandinavian Journal of Statistics 5 (3): 141–50. https://www.jstor.org/stable/4615704.

Abadi, Martín, Ashish Agarwal, Paul Barham, et al. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/.

Akritas, Michael G. 1994. “Nearest Neighbor Estimation of a Bivariate Distribution Under Random Censoring.” Ann. Statist. 22 (3): 1299–327. https://doi.org/10.1214/aos/1176325630.

Akritas, Michael G., and Michael P. LaValley. 2005. “A Generalized Product-Limit Estimator for Truncated Data.” Nonparametric Statistics, ahead of print, September. https://doi.org/10.1080/10485250500038637.

Alberge, Julie, Vincent Maladière, Olivier Grisel, Judith Abécassis, and Gaël Varoquaux. 2025. “Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks.” Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of machine learning research, vol. 258: 3619–27. https://proceedings.mlr.press/v258/alberge25a.html.

Allaire, J J, and François Chollet. 2020. keras: R Interface to ’Keras’. CRAN. https://cran.r-project.org/package=keras.

Allignol, Arthur, Jan Beyersmann, and Martin Schumacher. 2008. “Mvna an r Package for the Nelson-Aalen Estimator in Multistate Models.” R News 8 (2): 48–50.

Andersen, Per Kragh, Mette Gerster Hansen, and John P. Klein. 2004b. “Regression Analysis of Restricted Mean Survival Time Based on Pseudo-Observations.” Lifetime Data Analysis 10 (4): 335–50. https://doi.org/10.1007/s10985-004-4771-0.

Andersen, Per Kragh, Mette Gerster Hansen, and John P. Klein. 2004a. “Regression Analysis of Restricted Mean Survival Time Based on Pseudo-Observations.” Lifetime Data Analysis 10 (4): 335–50. https://doi.org/10.1007/s10985-004-4771-0.

Andersen, Per Kragh, John P. Klein, and Susanne Rosthøj. 2003. “Generalised Linear Models for Correlated Pseudo-Observations, with Applications to Multi-State Models.” Biometrika 90 (1): 15–27. https://www.jstor.org/stable/30042016.

Andersen, Per Kragh, and Maja Pohar Perme. 2010. “Pseudo-Observations in Survival Analysis.” Statistical Methods in Medical Research 19 (1): 71–99. https://doi.org/10.1177/0962280209105020.

Andres, Axel, Aldo Montano-Loza, Russell Greiner, et al. 2018. “A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis.” PLOS ONE 13 (3): e0193523. 10.1371/journal.pone.0193523.

Angus, John E. 1994. “The Probability Integral Transform and Related Results.” SIAM Review 36 (4): 652–54. http://www.jstor.org/stable/2132726.

Antolini, Laura, Patrizia Boracchi, and Elia Biganzoli. 2005. “A time-dependent discrimination index for survival data.” Statistics in Medicine 24 (24): 3927–44. https://doi.org/10.1002/sim.2427.

Argyropoulos, Christos, and Mark L. Unruh. 2015. “Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models.” PLoS ONE 10 (4): e0123784. https://doi.org/10.1371/journal.pone.0123784.

Austin, Peter C., and Jason P. Fine. 2017. “Practical Recommendations for Reporting Fine-Gray Model Analyses for Competing Risk Data.” Statistics in Medicine 36 (27): 4391–400. https://doi.org/10.1002/sim.7501.

Austin, Peter C., Frank E. Harrell Jr, and David van Klaveren. 2020. “Graphical Calibration Curves and the Integrated Calibration Index (ICI) for Survival Models.” Statistics in Medicine 39 (21): 2714–42. https://doi.org/10.1002/sim.8570.

Austin, Peter C., Hein Putter, Douglas S. Lee, and Ewout W. Steyerberg. 2022a. “Estimation of the Absolute Risk of Cardiovascular Disease and Other Events: Issues with the Use of Multiple Fine-Gray Subdistribution Hazard Models.” Circulation: Cardiovascular Quality and Outcomes 15 (2): e008368. https://doi.org/10.1161/CIRCOUTCOMES.121.008368.

Austin, Peter C, Hein Putter, Daniele Giardiello, and David van Klaveren. 2022b. “Graphical calibration curves and the integrated calibration index (ICI) for competing risk models.” Diagnostic and Prognostic Research 6 (1): 2. https://doi.org/10.1186/s41512-021-00114-6.

Avati, Anand, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, and Andrew Ng. 2020. “Countdown Regression: Sharp and Calibrated Survival Predictions.” Proceedings of Machine Learning Research, June, 145–55. https://proceedings.mlr.press/v115/avati20a.html.

Barnwal, Avinash, Hyunsu Cho, and Toby Hocking. 2022. “Survival Regression with Accelerated Failure Time Model in XGBoost.” Journal of Computational and Graphical Statistics 31 (4): 1292–302. https://doi.org/10.1080/10618600.2022.2067548.

Beaulac, Cédric, Jeffrey S. Rosenthal, Qinglin Pei, Debra Friedman, Suzanne Wolden, and David Hodgson. 2020. “An Evaluation of Machine Learning Techniques to Predict the Outcome of Children Treated for Hodgkin-Lymphoma on the AHOD0031 Trial.” Applied Artificial Intelligence 34 (14): 1100–1114. https://doi.org/10.1080/08839514.2020.1815151.

Benavoli, Alessio, Giorgio Corani, Janez Demšar, and Marco Zaffalon. 2017. “Time for a Change: A Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis.” Journal of Machine Learning Research 18 (77): 1–36. http://jmlr.org/papers/v18/16-305.html.

Bender, Andreas, Andreas Groll, and Fabian Scheipl. 2018. “A generalized additive model approach to time-to-event analysis.” Statistical Modelling 18 (3-4): 299–321. https://doi.org/10.1177/1471082X17748083.

Bender, Andreas, David Rügamer, Fabian Scheipl, and Bernd Bischl. 2021. “A General Machine Learning Framework for Survival Analysis.” In Machine Learning and Knowledge Discovery in Databases, edited by Frank Hutter, Kristian Kersting, Jefrey Lijffijt, and Isabel Valera. Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-030-67664-3_10.

Bender, Andreas, and Fabian Scheipl. 2018. “pammtools: Piece-wise exponential Additive Mixed Modeling tools.” arXiv:1806.01042 [Stat]. http://arxiv.org/abs/1806.01042.

Bender, Andreas, Fabian Scheipl, Wolfgang Hartl, Andrew G Day, and Helmut Küchenhoff. 2018. “Penalized estimation of complex, non-linear exposure-lag-response associations.” Biostatistics 20 (2): 315–31. https://doi.org/10.1093/biostatistics/kxy003.

Bennett, Steve. 1983. “Analysis of survival data by the proportional odds model.” Statistics in Medicine 2 (2): 273–77. https://doi.org/10.1002/sim.4780020223.

Bennis, Achraf, Sandrine Mouysset, and Mathieu Serrurier. 2020. “Estimation of Conditional Mixture Weibull Distribution with Right Censored Data Using Neural Network for Time-to-Event Analysis.” Advances in Knowledge Discovery and Data Mining (PAKDD). https://doi.org/10.1007/978-3-030-47426-3_53.

Bennis, Achraf, Sandrine Mouysset, and Mathieu Serrurier. 2021. “DPWTE: A Deep Learning Approach to Survival Analysis Using a Parsimonious Mixture of Weibull Distributions.” Artificial Neural Networks and Machine Learning (ICANN), 185–96. https://doi.org/10.1007/978-3-030-86340-1_15.

Beyersmann, Jan, Arthur Allignol, and Martin Schumacher. 2012. Competing Risks and Multistate Models with R. Use R! Springer.

Beygelzimer, Alina, Hal Daumé, John Langford, and Paul Mineiro. 2016. “Learning Reductions That Really Work.” Proceedings of the IEEE, no. 1 (January): 136–47. https://doi.org/10.1109/JPROC.2015.2494118.

Biganzoli, Elia, Patrizia Boracchi, Luigi Mariani, and Ettore Marubini. 1998. “Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach.” Statistics in Medicine 17 (10): 1169–86. https://doi.org/10.1002/(SICI)1097-0258(19980530)17:10<1169::AID-SIM796>3.0.CO;2-D.

Binder, Harald. 2013. CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks. CRAN.

Binder, Harald, Arthur Allignol, Martin Schumacher, and Jan Beyersmann. 2009. “Boosting for High-Dimensional Time-to-Event Data with Competing Risks.” Bioinformatics 25 (7): 890–96. https://doi.org/10.1093/bioinformatics/btp088.

Binder, Harald, and Martin Schumacher. 2008. “Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models.” BMC Bioinformatics 9 (1): 14. https://doi.org/10.1186/1471-2105-9-14.

Bischl, Bernd, O. Mersmann, H. Trautmann, and C. Weihs. 2012. “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation 20 (2): 249–75. https://doi.org/10.1162/EVCO_a_00069.

Bischl, Bernd, Raphael Sonabend, Lars Kotthoff, and Michel Lang, eds. 2024. Applied Machine Learning Using mlr3 in R. CRC Press. https://mlr3book.mlr-org.com.

Bishop, Christopher M. 2006. Pattern recognition and machine learning. Springer.

Blanche, Paul, Jean-François Dartigues, and Hélène Jacqmin-Gadda. 2013. “Review and comparison of ROC curve estimators for a time-dependent outcome with marker-dependent censoring.” Biometrical Journal 55 (5): 687–704. https://doi.org/10.1002/bimj.201200045.

Blanche, Paul, Aurélien Latouche, and Vivian Viallon. 2012. Time-dependent AUC with right-censored data: a survey study. October. https://doi.org/10.1007/978-1-4614-8981-8_11.

Bland, J Martin, and Douglas G. Altman. 2004. “The logrank test.” BMJ (Clinical Research Ed.) 328 (7447): 1073. https://doi.org/10.1136/bmj.328.7447.1073.

Bommert, Andrea, Thomas Welchowski, Matthias Schmid, and Jörg Rahnenführer. 2021. “Benchmark of Filter Methods for Feature Selection in High-Dimensional Gene Expression Survival Data.” Briefings in Bioinformatics 23 (1): bbab354. https://doi.org/10.1093/bib/bbab354.

Bonato, Vinicius, Veerabhadran Baladandayuthapani, Bradley M. Broom, Erik P. Sulman, Kenneth D. Aldape, and Kim-Anh Do. 2010. “Bayesian Ensemble Methods for Survival Prediction in Gene Expression Data.” Bioinformatics 27 (3): 359–67. https://doi.org/10.1093/bioinformatics/btq660.

Bonneville, Edouard F, Liesbeth C de Wreede, and Hein Putter. 2024. “Why You Should Avoid Using Multiple Fine–Gray Models: Insights from (Attempts at) Simulating Proportional Subdistribution Hazards Data.” Journal of the Royal Statistical Society Series A: Statistics in Society 187 (3): 580–93. https://doi.org/10.1093/jrsssa/qnae056.

Bouaziz, Olivier. 2023. “Fast Approximations of Pseudo-Observations in the Context of Right Censoring and Interval Censoring.” Biometrical Journal 65 (4): 2200071. https://doi.org/10.1002/bimj.202200071.

Bou-Hamad, Imad, Denis Larocque, and Hatem Ben-Ameur. 2011. “A review of survival trees.” Statist. Surv. 5: 44–71. https://doi.org/10.1214/09-SS047.

Bower, Hannah, Michael J Crowther, Mark J Rutherford, et al. 2019. “Capturing simple and complex time-dependent effects using flexible parametric survival models: A simulation study.” Communications in Statistics - Simulation and Computation, July, 1–17. https://doi.org/10.1080/03610918.2019.1634201.

Breiman, Leo. 1996. “Bagging Predictors.” Machine Learning 24 (2): 123–40. https://doi.org/10.1023/A:1018054314350.

Breiman, Leo, and Philip Spector. 1992. “Submodel Selection and Evaluation in Regression. The X-Random Case.” International Statistical Review / Revue Internationale de Statistique 60 (3): 291–319. https://doi.org/10.2307/1403680.

Breiman, L, J Friedman, C J Stone, and R A Olshen. 1984. Classification and Regression Trees. The Wadsworth and Brooks-Cole Statistics-Probability Series. Taylor & Francis. https://books.google.co.uk/books?id=JwQx-WOmSyQC.

Breslow, N. 1972. “Discussion following ‘Regression models and life tables’ by D. R. Cox.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 34 (2): 187–220.

Breslow, N. 1974. “Covariance Analysis of Censored Survival Data.” Biometrics 30 (1): 89–99. https://doi.org/10.2307/2529620.

Brier, Glenn. 1950. “Verification of forecasts expressed in terms of probability.” Monthly Weather Review 78 (1): 1–3.

Broström, Göran. 1987. “The Influence of Mother’s Death on Infant Mortality: A Case Study in Matched Data Survival Analysis.” Scandinavian Journal of Statistics 14 (2): 113–23. https://www.jstor.org/stable/4616055.

Broström, Göran. 2024. Eha: Event History Analysis. https://cran.r-project.org/package=eha.

Buckley, Jonathan, and Ian James. 1979. “Linear Regression with Censored Data.” Biometrika 66 (3): 429–36. https://doi.org/10.2307/2335161.

Buhlmann, Peter. 2006. “Boosting for high-dimensional linear models.” Ann. Statist. 34 (2): 559–83. https://doi.org/10.1214/009053606000000092.

Buhlmann, Peter, and Torsten Hothorn. 2007. “Boosting Algorithms: Regularization, Prediction and Model Fitting.” Statist. Sci. 22 (4): 477–505. https://doi.org/10.1214/07-STS242.

Bühlmann, Peter, and Bin Yu. 2003. “Boosting With the L2 Loss.” Journal of the American Statistical Association 98 (462): 324–39. https://doi.org/10.1198/016214503000125.

Burk, Lukas, John Zobolas, Bernd Bischl, Andreas Bender, Marvin N. Wright, and Raphael Sonabend. 2026. “A large-scale neutral comparison study of survival models on low-dimensional data.” Bioinformatics 42 (5): btag186. https://doi.org/10.1093/bioinformatics/btag186.

Candido dos Reis, Francisco J, Gordon C Wishart, Ed M Dicks, et al. 2017. “An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.” Breast Cancer Research 19 (1): 58. https://doi.org/10.1186/s13058-017-0852-3.

Casalicchio, Giuseppe, and Lukas Burk. 2024. “Evaluation and Benchmarking.” In Applied Machine Learning Using mlr3 in R, edited by Bernd Bischl, Raphael Sonabend, Lars Kotthoff, and Michel Lang. CRC Press. https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html.

Chambless, Lloyd E, and Guoqing Diao. 2006. “Estimation of time-dependent area under the ROC curve for long-term risk prediction.” Statistics in Medicine 25 (20): 3474–86. https://doi.org/10.1002/sim.2299.

Chandrashekar, Girish, and Ferat Sahin. 2014. “A Survey on Feature Selection Methods.” Computers & Electrical Engineering 40 (1): 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024.

Chen, George H. 2024. An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes. http://arxiv.org/abs/2410.01086.

Chen, Tianqi, Tong He, Michael Benesty, et al. 2020. xgboost: Extreme Gradient Boosting. CRAN. https://cran.r-project.org/package=xgboost.

Chen, Y. Q., and N. P. Jewell. 2001. “On a General Class of Semiparametric Hazards Regression Models.” Biometrika 88 (3): 687–702. https://doi.org/10.1093/biomet/88.3.687.

Chen, Yifei, Zhenyu Jia, Dan Mercola, and Xiaohui Xie. 2013. “A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index.” Computational and Mathematical Methods in Medicine 2013: 873595. https://doi.org/10.1155/2013/873595.

Ching, Travers, Xun Zhu, and Lana X. Garmire. 2018. “Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.” PLOS Computational Biology 14 (4): e1006076. https://doi.org/10.1371/journal.pcbi.1006076.

Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. 2010. “BART: Bayesian Additive Regression Trees.” The Annals of Applied Statistics 4 (1). https://doi.org/10.1214/09-aoas285.

Cho, Kyunghyun, Bart van Merriënboer, Caglar Gulcehre, et al. 2014. “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.” Proceedings of EMNLP. https://arxiv.org/abs/1406.1078.

Chollet, François. 2018. Deep Learning with Python. Manning Publications.

Choodari-Oskooei, Babak, Patrick Royston, and Mahesh K. B. Parmar. 2012a. “A simulation study of predictive ability measures in a survival model I: Explained variation measures.” Statistics in Medicine 31 (23): 2627–43. https://doi.org/10.1002/sim.4242.

Choodari-Oskooei, Babak, Patrick Royston, and Mahesh K. B. Parmar. 2012b. “A simulation study of predictive ability measures in a survival model II: explained randomness and predictive accuracy.” Statistics in Medicine 31 (23): 2644–59. https://doi.org/10.1002/sim.4242.

Christodoulou, Evangelia, Jie Ma, Gary S Collins, Ewout W Steyerberg, Jan Y Verbakel, and Ben Van Calster. 2019. “A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.” Journal of Clinical Epidemiology 110 (June): 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004.

Ciampi, Antonio, Sheilah A Hogg, Steve McKinney, and Johanne Thiffault. 1988. “RECPAM: a computer program for recursive partition and amalgamation for censored survival data and other situations frequently occurring in biostatistics. I. Methods and program features.” Computer Methods and Programs in Biomedicine 26 (3): 239–56. https://doi.org/10.1016/0169-2607(88)90004-1.

Ciampi, Antonio, Johanne Thiffault, Jean Pierre Nakache, and Bernard Asselain. 1986. “Stratification by stepwise regression, correspondence analysis and recursive partition: a comparison of three methods of analysis for survival data with covariates.” Computational Statistics and Data Analysis 4 (3): 185–204. https://doi.org/10.1016/0167-9473(86)90033-2.

Collett, David. 2014. Modelling Survival Data in Medical Research. 3rd ed. CRC.

Cook, Richard J., and Jerald F. Lawless. 2007. The Statistical Analysis of Recurrent Events. Statistics for Biology and Health. Springer. https://doi.org/10.1007/978-0-387-69810-6.

Cortes, Corinna, and Vladimir Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20: 273–97. https://doi.org/10.1007/BF00994018.

Cox, D. R. 1972. “Regression Models and Life-Tables.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 34 (2): 187–220.

Cox, D. R. 1975. “Partial Likelihood.” Biometrika 62 (August): 269–76. https://doi.org/10.1093/biomet/62.2.269.

Craig, Erin, Chenyang Zhong, and Robert Tibshirani. 2025. “A Review of Survival Stacking: A Method to Cast Survival Regression Analysis as a Classification Problem.” The International Journal of Biostatistics 21 (1): 37–51. https://doi.org/10.1515/ijb-2022-0055.

D’Agostino, R. B., and Byung-Ho Nam. 2003. “Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration Measures.” In Advances in Survival Analysis, vol. 23. Handbook of Statistics. Elsevier. https://doi.org/10.1016/S0169-7161(03)23001-7.

Dawid, A P. 1984. “Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach.” Journal of the Royal Statistical Society. Series A (General) 147 (2): 278–92. https://doi.org/10.2307/2981683.

Dawid, A Philip. 1986. “Probability Forecasting.” Encyclopedia of Statistical Sciences 7: 210–18.

Dawid, Alexander Philip, and Monica Musio. 2014. “Theory and Applications of Proper Scoring Rules.” METRON 72: 169–83. https://doi.org/10.1007/s40300-014-0039-y.

de Wreede, Liesbeth C., Marta Fiocco, and Hein Putter. 2011. “mstate: An R Package for the Analysis of Competing Risks and Multi-State Models.” Journal of Statistical Software 38 (7): 1–30. https://doi.org/10.18637/jss.v038.i07.

Demler, Olga V, Nina P Paynter, and Nancy R Cook. 2015. “Tests of calibration and goodness-of-fit in the survival setting.” Statistics in Medicine 34 (10): 1659–80. https://doi.org/10.1002/sim.6428.

Demšar, Janez. 2006. “Statistical comparisons of classifiers over multiple data sets.” Journal of Machine Learning Research 7 (1): 1–30.

Dietterich, Thomas G. 1998. “Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms.” Neural Computation 10 (7): 1895–923. https://doi.org/10.1162/089976698300017197.

Djangang, Paul M., Summer S. Han, and Nilotpal Sanyal. 2025. Comparative Review of Modern Competing Risk Methods in High-Dimensional Settings. https://arxiv.org/abs/2503.12824.

Efron, Bradley. 1977. “The Efficiency of Cox’s Likelihood Function for Censored Data.” Journal of the American Statistical Association 72 (359): 557–65. https://doi.org/10.1080/01621459.1977.10480613.

Efron, Bradley, and Robert Tibshirani. 1997. “Improvements on Cross-Validation: The .632+ Bootstrap Method.” Journal of the American Statistical Association 92 (438): 548–60. http://www.jstor.org/stable/2965703.

Eilers, Paul H. C., and Brian D. Marx. 1996. “Flexible Smoothing with B-splines and Penalties.” Statistical Science 11 (2): 89–121. https://doi.org/10.1214/ss/1038425655.

Elsken, Thomas, Jan Hendrik Metzen, and Frank Hutter. 2019. “Neural Architecture Search: A Survey.” Journal of Machine Learning Research 20 (55): 1–21. https://jmlr.org/papers/v20/18-598.html.

Evers, Ludger, and Claudia-Martina Messow. 2008. “Sparse kernel methods for high-dimensional survival data.” Bioinformatics 24 (14): 1632–38.

Fine, Jason P., and Robert J. Gray. 1999. “A Proportional Hazards Model for the Subdistribution of a Competing Risk.” Journal of the American Statistical Association 94 (446): 496–509. http://www.jstor.org/stable/2670170.

Fleming, Thomas R, Judith R O’Fallon, Peter C O’Brien, and David P Harrington. 1980. “Modified Kolmogorov-Smirnov Test Procedures with Application to Arbitrarily Right-Censored Data.” Biometrics 36 (4): 607–25. https://doi.org/10.2307/2556114.

Fotso, Stephane. 2018. “Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework.” arXiv Preprint arXiv:1801.05512, January. http://arxiv.org/abs/1801.05512.

Fouodo, Cesaire J K, I Konig, C Weihs, A Ziegler, and M Wright. 2018. “Support vector machines for survival analysis with R.” The R Journal 10 (July): 412–23.

Friedman, Jerome. 1999. “Stochastic Gradient Boosting.” Computational Statistics & Data Analysis 38 (March): 367–78. https://doi.org/10.1016/S0167-9473(01)00065-2.

Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (5): 1189–232. http://www.jstor.org/stable/2699986.

Friedman, Michael. 1982. “Piecewise exponential models for survival data with covariates.” The Annals of Statistics 10 (1): 101–13.

Gandy, A., and T. J. Matcham. 2025. “A Proper Concordance Index for Models with Crossing Hazards.” Scandinavian Journal of Statistics 52 (4): 1479–504. https://doi.org/10.1111/sjos.70000.

Gefeller, Olaf, and Holger Dette. 1992. “Nearest Neighbour Kernel Estimation of the Hazard Function from Censored Data.” Journal of Statistical Computation and Simulation 43 (1-2): 93–101. https://doi.org/10.1080/00949659208811430.

Geloven, Nan van, Daniele Giardiello, Edouard F Bonneville, et al. 2022. “Validation of Prediction Models in the Presence of Competing Risks: A Guide Through Modern Methods.” BMJ 377. https://doi.org/10.1136/bmj-2021-069249.

Gensheimer, Michael F., and Balasubramanian Narasimhan. 2019a. “A Scalable Discrete-Time Survival Model for Neural Networks.” PeerJ 7: e6257. https://doi.org/10.7717/peerj.6257.

Gensheimer, Michael F, and Balasubramanian Narasimhan. 2019b. “A scalable discrete-time survival model for neural networks.” PeerJ 7: e6257.

Gerds, Thomas Alexander, and Brice Ozenne. 2019. riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks. CRAN. https://cran.r-project.org/package=riskRegression.

Gerds, Thomas A, and Martin Schumacher. 2006. “Consistent Estimation of the Expected Brier Score in General Survival Models with Right-Censored Event Times.” Biometrical Journal 48 (6): 1029–40. https://doi.org/10.1002/bimj.200610301.

Géron, Aurélien. 2019. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O’Reilly. https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.

Giolo, Suely Ruiz. 2004. “Turnbull’s Nonparametric Estimator for Interval-Censored Data.” Departamento de Estatística, Universidade Federal Do Paraná, Technical Report. https://docs.ufpr.br/~giolo/CE063/Artigos/A4_Turnbull.pdf.

Gneiting, Tilmann, and Adrian E Raftery. 2007. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association 102 (477): 359–78. https://doi.org/10.1198/016214506000001437.

Goli, Shahrbanoo, Hossein Mahjub, Javad Faradmal, Hoda Mashayekhi, and Ali-Reza Soltanian. 2016. “Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression.” Computational and Mathematical Methods in Medicine 2016: 2157984. https://doi.org/10.1155/2016/2157984.

Goli, Shahrbanoo, Hossein Mahjub, Javad Faradmal, and Ali-Reza Soltanian. 2016. “Performance Evaluation of Support Vector Regression Models for Survival Analysis: A Simulation Study.” International Journal of Advanced Computer Science and Applications 7 (June). https://doi.org/10.14569/IJACSA.2016.070650.

Gompertz, Benjamin. 1825. “On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies.” Philosophical Transactions of the Royal Society of London 115: 513–83.

Gonzalez Ginestet, Pablo, Ales Kotalik, David M. Vock, Julian Wolfson, and Erin E. Gabriel. 2021. “Stacked Inverse Probability of Censoring Weighted Bagging: A Case Study In the InfCareHIV Register.” Journal of the Royal Statistical Society Series C: Applied Statistics 70 (1): 51–65. https://doi.org/10.1111/rssc.12448.

Good, I J. 1952. “Rational Decisions.” Journal of the Royal Statistical Society. Series B (Methodological) 14 (1): 107–14. http://www.jstor.org/stable/2984087.

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.

Gordon, Louis, and Richard A Olshen. 1985. “Tree-structured survival analysis.” Cancer Treatment Reports 69 (10): 1065–69.

Graf, Erika, Claudia Schmoor, Willi Sauerbrei, and Martin Schumacher. 1999. “Assessment and comparison of prognostic classification schemes for survival data.” Statistics in Medicine 18 (17-18): 2529–45. https://doi.org/10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5.

Graf, Erika, and Martin Schumacher. 1995. “An Investigation on Measures of Explained Variation in Survival Analysis.” Journal of the Royal Statistical Society. Series D (The Statistician) 44 (4): 497–507. https://doi.org/10.2307/2348898.

Grambauer, Nadine, Martin Schumacher, and Jan Beyersmann. 2010. “Proportional subdistribution hazards modeling offers a summary analysis, even if misspecified.” Statistics in Medicine 29 (7): 875–84. https://doi.org/10.1002/sim.3786.

Gray, Robert J. 1988. “A Class of K-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk.” The Annals of Statistics 16 (3): 1141–54. https://doi.org/10.1214/aos/1176350951.

Greenwood, M. 1926. A Report on the Natural Duration of Cancer. London: H.M.S.O.

Guyon, Isabelle, and André Elisseeff. 2003. “An Introduction to Variable and Feature Selection.” The Journal of Machine Learning Research 3 (March): 1157–82.

Habibi, Danial, Mohammad Rafiei, Ali Chehrei, Zahra Shayan, and Soheil Tafaqodi. 2018. “Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients.” Asian Pacific Journal of Cancer Prevention : APJCP 19 (3): 749–53. https://doi.org/10.22034/APJCP.2018.19.3.749.

Haider, Humza, Bret Hoehn, Sarah Davis, and Russell Greiner. 2020. “Effective ways to build and evaluate individual survival distributions.” Journal of Machine Learning Research 21 (85): 1–63.

Han, Kyunghwa, and Inkyung Jung. 2022. “Restricted Mean Survival Time for Survival Analysis: A Quick Guide for Clinical Researchers.” Korean Journal of Radiology 23 (5): 495–99. https://doi.org/10.3348/kjr.2022.0061.

Hao, Jie, Youngsoon Kim, Tejaswini Mallavarapu, Jung Hun Oh, and Mingon Kang. 2018. Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis. 381–86. https://doi.org/10.1109/BIBM.2018.8621345.

Hardin, James W., and Joseph M. Hilbe. 2012. Generalized Estimating Equations. 2nd ed. Chapman; Hall/CRC. https://doi.org/10.1201/b13880.

Harrell, F E Jr, K L Lee, R M Califf, D B Pryor, and R A Rosati. 1984. “Regression modelling strategies for improved prognostic prediction.” Statistics in Medicine 3 (2): 143–52. https://doi.org/10.1002/sim.4780030207.

Harrell, Frank E., Robert M. Califf, and David B. Pryor. 1982. “Evaluating the yield of medical tests.” JAMA 247 (18): 2543–46. https://doi.org/http://dx.doi.org/10.1001/jama.1982.03320430047030.

Harrington, David P., and Thomas R. Fleming. 1982. “A class of rank test procedures for censored survival data.” Biometrika 69 (3): 553–60. https://doi.org/10.1093/biomet/69.3.553.

Hartman, Nicholas, Sehee Kim, Kevin He, and John D. Kalbfleisch. 2022. “Concordance Indices with Left-Truncated and Right-Censored Data.” Biometrics 79 (3): 1624–34. https://doi.org/10.1111/biom.13714.

Hastie, Trevor, and Robert Tibshirani. 1990. “Exploring the Nature of Covariate Effects in the Proportional Hazards Model.” Biometrics 46 (4): 1005–16. https://doi.org/10.2307/2532444.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2001. The Elements of Statistical Learning. Springer New York Inc.

Heagerty, Patrick J., Thomas Lumley, and Margaret S. Pepe. 2000. “Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker.” Biometrics 56 (2): 337–44. https://doi.org/10.1111/j.0006-341X.2000.00337.x.

Heagerty, Patrick J, and Yingye Zheng. 2005. “Survival Model Predictive Accuracy and ROC Curves.” Biometrics 61 (1): 92–105. https://doi.org/10.1111/j.0006-341X.2005.030814.x.

Hendrycks, Dan, and Kevin Gimpel. 2016. Gaussian Error Linear Units (GELUs). https://arxiv.org/abs/1606.08415.

Herrmann, Moritz, Philipp Probst, Roman Hornung, Vindi Jurinovic, and Anne-Laure Boulesteix. 2021. “Large-scale benchmark study of survival prediction methods using multi-omics data.” Briefings in Bioinformatics 22 (3). https://doi.org/10.1093/bib/bbaa167.

Hielscher, Thomas, Manuela Zucknick, Wiebke Werft, and Axel Benner. 2010. “On the Prognostic Value of Gene Expression Signatures for Censored Data BT - Advances in Data Analysis, Data Handling and Business Intelligence.” Edited by Andreas Fink, Berthold Lausen, Wilfried Seidel, and Alfred Ultsch. Springer Berlin Heidelberg. https://doi.org/10.1002/sim.3768.

Hinchliffe, Sally R, and Paul C Lambert. 2013. “Flexible Parametric Modelling of Cause-Specific Hazards to Estimate Cumulative Incidence Functions.” BMC Medical Research Methodology 13: 13. https://doi.org/10.1186/1471-2288-13-13.

Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” Science 313 (5786): 504–7. https://doi.org/10.1126/science.1127647.

Ho, Jonathan, Ajay Jain, and Pieter Abbeel. 2020. “Denoising Diffusion Probabilistic Models.” Advances in Neural Information Processing Systems (NeurIPS). https://arxiv.org/abs/2006.11239.

Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-term Memory.” Neural Computation 9 (December): 1735–80. https://doi.org/10.1162/neco.1997.9.8.1735.

Hornik, Kurt. 1991. “Approximation capabilities of multilayer feedforward networks.” Neural Networks 4 (2): 251–57. https://doi.org/10.1016/0893-6080(91)90009-T.

Hornik, Kurt, Maxwell Stinchcombe, and Halbert White. 1989. “Multilayer feedforward networks are universal approximators.” Neural Networks 2 (5): 359–66. https://doi.org/10.1016/0893-6080(89)90020-8.

Hornung, Roman, Malte Nalenz, Lennart Schneider, et al. 2023. Evaluating Machine Learning Models in Non-Standard Settings: An Overview and New Findings. https://arxiv.org/abs/2310.15108.

Hosmer, David W, and Stanley Lemeshow. 1980. “Goodness of fit tests for the multiple logistic regression model.” Communications in Statistics-Theory and Methods 9 (10): 1043–69.

Hosmer Jr, David W, Stanley Lemeshow, and Susanne May. 2011. Applied survival analysis: regression modeling of time-to-event data. Vol. 618. John Wiley & Sons.

Hothorn, Torsten. 2021. “Most Likely Transformations: The Mlt Package.” Journal of Statistical Software 98 (11): 1–34. https://doi.org/10.18637/jss.v098.i11.

Hothorn, Torsten, Peter Buehlmann, Thomas Kneib, Matthias Schmid, and Benjamin Hofner. 2020. mboost: Model-Based Boosting. CRAN. https://cran.r-project.org/package=mboost.

Hothorn, Torsten, Peter Bühlmann, Sandrine Dudoit, Annette Molinaro, and Mark J Van Der Laan. 2005. “Survival ensembles.” Biostatistics 7 (3): 355–73. https://doi.org/10.1093/biostatistics/kxj011.

Hothorn, Torsten, and Berthold Lausen. 2003. “On the exact distribution of maximally selected rank statistics.” Computational Statistics & Data Analysis 43 (2): 121–37. https://doi.org/10.1016/S0167-9473(02)00225-6.

Hothorn, Torsten, Berthold Lausen, Axel Benner, and Martin Radespiel-Tröger. 2004. “Bagging survival trees.” Statistics in Medicine 23 (1): 77–91. https://doi.org/10.1002/sim.1593.

Houwelingen, Hans C. van, and Hein Putter. 2011. Dynamic Prediction in Clinical Survival Analysis. 1st ed. CRC Press. https://doi.org/10.1201/b11311.

Huang, Shigao, Jie Yang, Simon Fong, and Qi Zhao. 2020. “Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.” Cancer Letters 471: 61–71. https://doi.org/10.1016/j.canlet.2019.12.007.

Hung, Hung, and Chin-Tsang Chiang. 2010. “Estimation methods for time-dependent AUC models with survival data.” The Canadian Journal of Statistics / La Revue Canadienne de Statistique 38 (1): 8–26. http://www.jstor.org/stable/27805213.

Hurvich, Clifford M, and Chih-Ling Tsai. 1989. “Regression and time series model selection in small samples.” Biometrika 76 (2): 297–307. https://doi.org/10.1093/biomet/76.2.297.

Ibe, Oliver C. 2013. Markov Processes for Stochastic Modeling. 2nd ed. Academic Press. https://www.sciencedirect.com/book/9780124077959/markov-processes-for-stochastic-modeling.

Ishwaran, Hemant, Eugene H Blackstone, Claire E Pothier, and Michael S Lauer. 2004. “Relative Risk Forests for Exercise Heart Rate Recovery as a Predictor of Mortality.” Journal of the American Statistical Association 99 (467): 591–600. https://doi.org/10.1198/016214504000000638.

Ishwaran, Hemant, Thomas A. Gerds, Udaya B. Kogalur, Richard D. Moore, Stephen J. Gange, and Bryan M. Lau. 2014. “Random Survival Forests for Competing Risks.” Biostatistics 15 (4): 757–73. https://doi.org/10.1093/biostatistics/kxu010.

Ishwaran, Hemant, and Udaya B Kogalur. 2018. randomForestSRC. https://cran.r-project.org/package=randomForestSRC.

Ishwaran, Hemant, Udaya B. Kogalur, Eugene H. Blackstone, and Michael S. Lauer. 2008. “Random survival forests.” The Annals of Applied Statistics 2 (3): 841–60. https://doi.org/10.1214/08-AOAS169.

Ishwaran, Hemant, Udaya B. Kogalur, Xi Chen, and Andy J. Minn. 2011. “Random Survival Forests for High-Dimensional Data.” Statistical Analysis and Data Mining: The ASA Data Science Journal 4 (1): 115–32. https://doi.org/10.1002/sam.10103.

Jackson, Christopher. 2016. “flexsurv: A Platform for Parametric Survival Modeling in R.” Journal of Statistical Software 70 (8): 1–33.

Jaeger, Byron C., Sawyer Welden, Kristin Lenoir, et al. 2024. “Accelerated and Interpretable Oblique Random Survival Forests.” Journal of Computational and Graphical Statistics 33 (1): 192–207. https://doi.org/10.1080/10618600.2023.2231048.

Jager, Kitty J, Paul C van Dijk, Carmine Zoccali, and Friedo W Dekker. 2008. “The analysis of survival data: the Kaplan–Meier method.” Kidney International 74 (5): 560–65. https://doi.org/10.1038/ki.2008.217.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An introduction to statistical learning. Vol. 112. Springer.

Johnson, Brent A, and Qi Long. 2011. “Survival ensembles by the sum of pairwise differences with application to lung cancer microarray studies.” Ann. Appl. Stat. 5 (2A): 1081–101. https://doi.org/10.1214/10-AOAS426.

Kalbfleisch, J. D., and R. L. Prentice. 1973. “Marginal likelihoods based on Cox’s regression and life model.” Biometrika 60 (2): 267–78. https://doi.org/10.1093/biomet/60.2.267.

Kalbfleisch, John D, and Ross L Prentice. 1980. The statistical analysis of failure time data. John Wiley & Sons.

Kamarudin, Adina Najwa, Trevor Cox, and Ruwanthi Kolamunnage-Dona. 2017. “Time-dependent ROC curve analysis in medical research: Current methods and applications.” BMC Medical Research Methodology 17 (1): 1–19. https://doi.org/10.1186/s12874-017-0332-6.

Kantidakis, Georgios, Hein Putter, Saskia Litière, and Marta Fiocco. 2023. “Statistical Models Versus Machine Learning for Competing Risks: Development and Validation of Prognostic Models.” BMC Medical Research Methodology 23: 51. https://doi.org/10.1186/s12874-023-01866-z.

Kaplan, E. L., and Paul Meier. 1958. “Nonparametric Estimation from Incomplete Observations.” Journal of the American Statistical Association 53 (282): 457–81. https://doi.org/10.2307/2281868.

Katzman, Jared L, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. 2018. “DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.” BMC Medical Research Methodology 18 (1): 24. https://doi.org/10.1186/s12874-018-0482-1.

Khan, Faisal M., and Valentina Bayer Zubek. 2008. “Support vector regression for censored data (SVRc): A novel tool for survival analysis.” Proceedings - IEEE International Conference on Data Mining, ICDM, 863–68. https://doi.org/10.1109/ICDM.2008.50.

Kim, Sunkyu, Keonwoo Kim, Junseok Choe, Inggeol Lee, and Jaewoo Kang. 2020. “Improved survival analysis by learning shared genomic information from pan-cancer data.” Bioinformatics 36 (Supplement_1): i389–98. https://doi.org/10.1093/bioinformatics/btaa462.

Kingma, Diederik P., and Jimmy Ba. 2015. “Adam: A Method for Stochastic Optimization.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1412.6980.

Kingma, Diederik P., and Max Welling. 2014. “Auto-Encoding Variational Bayes.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1312.6114.

Kirmani, S N U A, and Ramesh C Gupta. 2001. “On the Proportional Odds Model in Survival Analysis.” Annals of the Institute of Statistical Mathematics 53 (2): 203–16. https://doi.org/10.1023/A:1012458303498.

Klein, John P, and Melvin L Moeschberger. 2003. Survival analysis: techniques for censored and truncated data. 2nd ed. Springer Science & Business Media.

Kleinbaum, David G, and Mitchel Klein. 1996. Survival Analysis a Self-Learning Text. Springer.

Kohavi, Ron. 1995. “A study of cross-validation and bootstrap for accuracy estimation and model selection.” Ijcai 14 (2): 1137–45.

Kopper, Philipp, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, and David Rügamer. 2021. “Semi-Structured Deep Piecewise Exponential Models.” Proceedings of AAAI Spring Symposium on Survival Prediction: Algorithms, Challenges, and Applications, Proceedings of machine learning research, vol. 146: 40–53.

Kopper, Philipp, David Rügamer, Raphael Sonabend, Bernd Bischl, and Andreas Bender. 2026. “On Training Survival Models with Scoring Rules.” In Machine Learning and Knowledge Discovery in Databases. Research Track, edited by Rita P. Ribeiro, Bernhard Pfahringer, Nathalie Japkowicz, et al. Springer Nature Switzerland.

Kopper, Philipp, Simon Wiegrebe, Bernd Bischl, Andreas Bender, and David Rügamer. 2022. “DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis.” In Advances in Knowledge Discovery and Data Mining, edited by João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, and Fei Teng. Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-031-05936-0_20.

Korn, Edward L., and Richard Simon. 1990. “Measures of explained variation for survival data.” Statistics in Medicine 9 (5): 487–503. https://doi.org/10.1002/sim.4780090503.

Korn, Edward L, and Richard Simon. 1991. “Explained Residual Variation, Explained Risk, and Goodness of Fit.” The American Statistician 45 (3): 201–6. https://doi.org/10.2307/2684290.

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems, edited by F. Pereira, C. J. Burges, L. Bottou, and K. Q. Weinberger, vol. 25. Curran Associates, Inc.

Krzyziński, Mateusz, Mikołaj Spytek, Hubert Baniecki, and Przemysław Biecek. 2023. “SurvSHAP(t): Time-Dependent Explanations of Machine Learning Survival Models.” Knowledge-Based Systems 262: 110234. https://doi.org/10.1016/j.knosys.2022.110234.

Kuhn, Max, and Julia Silge. 2023. Tidy Modeling with R. https://www.tmwr.org/.

Kvamme, Håvard, and Ørnulf Borgan. 2021. “Continuous and discrete-time survival prediction with neural networks.” Lifetime Data Analysis 27 (4): 710–36. https://doi.org/10.1007/s10985-021-09532-6.

Kvamme, Håvard, and Ørnulf Borgan. 2023. “The Brier Score Under Administrative Censoring: Problems and a Solution.” Journal of Machine Learning Research 24 (2): 1–26. http://jmlr.org/papers/v24/19-1030.html.

Kvamme, Håvard, Ørnulf Borgan, and Ida Scheel. 2019. “Time-to-event prediction with neural networks and Cox regression.” Journal of Machine Learning Research 20 (129): 1–30.

Laan, Mark K van der, Eric C Polley, and Alan E Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6 (1). https://doi.org/10.2202/1544-6115.1309.

Lambert, Paul C. 2007. “Modeling of the cure fraction in survival studies.” The Stata Journal 7 (3): 351–75.

Land, Walker H, Xingye Qiao, Dan Margolis, and Ron Gottlieb. 2011. “A new tool for survival analysis: evolutionary programming/evolutionary strategies (EP/ES) support vector regression hybrid using both censored / non-censored (event) data.” Procedia Computer Science 6: 267–72. https://doi.org/10.1016/j.procs.2011.08.050.

Lang, Michel, Martin Binder, Jakob Richter, et al. 2019. “mlr3: A modern object-oriented machine learning framework in R.” Journal of Open Source Software 4 (44): 1903. https://doi.org/10.21105/joss.01903.

Langbein, Sophie Hanna, Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek, and Marvin N. Wright. 2025. “Interpretable Machine Learning for Survival Analysis.” Biometrical Journal 67 (6): e70089. https://doi.org/10.1002/bimj.70089.

LeBlanc, Michael, and John Crowley. 1992. “Relative Risk Trees for Censored Survival Data.” Biometrics 48 (2): 411–25. https://doi.org/10.2307/2532300.

LeBlanc, Michael, and John Crowley. 1993. “Survival Trees by Goodness of Split.” Journal of the American Statistical Association 88 (422): 457–67. https://doi.org/10.2307/2290325.

LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep learning.” Nature 521 (7553): 436–44. https://doi.org/10.1038/nature14539.

LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86 (11): 2278–324. https://doi.org/10.1109/5.726791.

Lee, Changhee, William Zame, Jinsung Yoon, and Mihaela Van der Schaar. 2018. “DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks.” Proceedings of the AAAI Conference on Artificial Intelligence 32 (1). https://doi.org/10.1609/aaai.v32i1.11842.

Lee, Donald K. K., Ningyuan Chen, and Hemant Ishwaran. 2021. “Boosted nonparametric hazards with time-dependent covariates.” The Annals of Statistics 49 (4): 2101–28. https://doi.org/10.1214/20-AOS2028.

Lévesque, Linda E, James A Hanley, Abbas Kezouh, and Samy Suissa. 2010. “Problem of Immortal Time Bias in Cohort Studies: Example Using Statins for Preventing Progression of Diabetes.” BMJ 340. https://doi.org/10.1136/bmj.b5087.

Li, Jialiang, and Shuangge Ma. 2011. “Time-Dependent ROC Analysis Under Diverse Censoring Patterns.” Statistics in Medicine 30 (11): 1266–77. https://doi.org/10.1002/sim.4178.

Li, Liang, Tom Greene, and Bo Hu. 2018. “A simple method to estimate the time-dependent receiver operating characteristic curve and the area under the curve with right censored data.” Statistical Methods in Medical Research 27 (8): 2264–78. https://doi.org/10.1177/0962280216680239.

Liang, Hua, and Guohua Zou. 2008. “Improved AIC Selection Strategy for Survival Analysis.” Computational Statistics & Data Analysis 52 (5): 2538–48. https://doi.org/10.1016/j.csda.2007.09.003.

Lin, D. Y. 2007. “On the Breslow Estimator.” Lifetime Data Analysis 13 (4): 471–80. https://doi.org/10.1007/s10985-007-9048-y.

Loh, De Rong, Elliot D Hill, Nan Liu, Geraldine Dawson, and Matthew M Engelhard. 2025. “Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis.” JMIR AI 4 (March): e62985. https://doi.org/10.2196/62985.

Loprinzi, C L, J A Laurie, H S Wieand, et al. 1994. “Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group.” Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology 12 (3): 601–7. https://doi.org/10.1200/JCO.1994.12.3.601.

Lundberg, Scott M, and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems 30.

Luxhoj, James T., and Huan Jyh Shyur. 1997. “Comparison of proportional hazards models and neural networks for reliability estimation.” Journal of Intelligent Manufacturing 8 (3): 227–34. https://doi.org/10.1023/A:1018525308809.

Ma, Shuangge, and Jian Huang. 2006. “Regularized ROC method for disease classification and biomarker selection with microarray data.” Bioinformatics (Oxford, England) 21 (January): 4356–62. https://doi.org/10.1093/bioinformatics/bti724.

Maller, Ross A., and Xian Zhou. 1996. Survival Analysis with Long-Term Survivors. Wiley.

Mantel, N., N. R. Bohidar, and J. L. Ciminera. 1977. “Mantel-Haenszel analyses of litter-matched time to response data, with modifications for recovery of interlitter information.” Cancer Research 37: 3863–68.

Mayr, Andreas, Benjamin Hofner, and Matthias Schmid. 2016. “Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection.” BMC Bioinformatics 17 (1): 288. https://doi.org/10.1186/s12859-016-1149-8.

Mayr, Andreas, and Matthias Schmid. 2014. “Boosting the concordance index for survival data–a unified framework to derive and evaluate biomarker combinations.” PloS One 9 (1): e84483–83. https://doi.org/10.1371/journal.pone.0084483.

McGough, Sarah F., Devin Incerti, Svetlana Lyalina, Ryan Copping, Balasubramanian Narasimhan, and Robert Tibshirani. 2021. “Penalized Regression for Left-Truncated and Right-Censored Survival Data.” Statistics in Medicine 40 (25): 5487–500. https://doi.org/10.1002/sim.9136.

Mogensen, Ulla B., and Thomas A. Gerds. 2013. “A Random Forest Approach for Competing Risks Based on Pseudo-Values.” Statistics in Medicine 32 (18): 3102–14. https://doi.org/10.1002/sim.5775.

Mogensen, Ulla B, Hemant Ishwaran, and Thomas A Gerds. 2012. “Evaluating Random Forests for Survival Analysis Using Prediction Error Curves.” Journal of Statistical Software 50 (11). https://doi.org/10.18637/jss.v050.i11.

Moghimi-dehkordi, Bijan, Azadeh Safaee, Mohamad Amin Pourhoseingholi, Reza Fatemi, Ziaoddin Tabeie, and Mohammad Reza Zali. 2008. “Statistical Comparison of Survival Models for Analysis of Cancer Data.” Asian Pacific Journal of Cancer Prevention 9: 417–20.

Molnar, Christoph. 2019. Interpretable Machine Learning. https://christophm.github.io/interpretable-ml-book/.

Mondol, Raktim Kumar, Ewan K. A. Millar, Arcot Sowmya, and Erik Meijering. 2024. “BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.” IEEE Journal of Biomedical and Health Informatics 28 (9): 5290–302. https://doi.org/10.1109/jbhi.2024.3418341.

Monterrubio-Gómez, Karla, Nathan Constantine-Cooke, and Catalina A. Vallejos. 2024. “A Review on Statistical and Machine Learning Competing Risks Methods.” Biometrical Journal 66 (2): 2300060. https://doi.org/10.1002/bimj.202300060.

Morales-Hernández, Alejandro, Inneke Van Nieuwenhuyse, and Sebastian Rojas Gonzalez. 2023. “A survey on multi-objective hyperparameter optimization algorithms for machine learning.” Artificial Intelligence Review 56 (8): 8043–93. https://doi.org/10.1007/s10462-022-10359-2.

Muldowney, Pat, Krzysztof Ostaszewski, and Wojciech Wojdowski. 2012. “The Darth Vader Rule.” Tatra Mountains Mathematical Publications 52 (1): 53–63. https://doi.org/10.2478/tatra.v52i0.178.

Müller, Hans-Georg, and Jane-Ling Wang. 1994. “Hazard Rate Estimation Under Random Censoring with Varying Kernels and Bandwidths.” Biometrics 50 (1): 61–76. http://www.jstor.org/stable/2533197.

Murphy, Allan H. 1973. “A New Vector Partition of the Probability Score.” Journal of Applied Meteorology and Climatology (Boston MA, USA) 12 (4): 595–600. https://doi.org/10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2.

Nadeau, Claude, and Yoshua Bengio. 2003. “Inference for the Generalization Error.” Machine Learning 52 (3): 239–81. https://doi.org/10.1023/A:1024068626366.

Nagpal, Chirag, Xinyu Li, and Artur Dubrawski. 2021. “Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks.” IEEE Journal of Biomedical and Health Informatics 25 (8): 3163–75. https://doi.org/10.1109/JBHI.2021.3052441.

Nagpal, Chirag, Steve Yadlowsky, Negar Rostamzadeh, and Katherine Heller. 2021. “Deep Cox Mixtures for Survival Regression.” Proceedings of the 6th Machine Learning for Healthcare Conference (MLHC). https://arxiv.org/abs/2101.06536.

Nair, Vinod, and Geoffrey E Hinton. 2010. “Rectified linear units improve restricted boltzmann machines.” Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807–14.

Nelson, Wayne. 1972. “Theory and Applications of Hazard Plotting for Censored Failure Data.” Technometrics 14 (4): 945–66.

Nießl, Alexandra, Arthur Allignol, Carina Mueller, and Jan Beyersmann. 2023. “Statistical Inference for State Occupation and Transition Probabilities in Non-Markov Multi-State Models Subject to Both Random Left-Truncation and Right-Censoring.” Econometrics and Statistics 25: 110–24. https://doi.org/10.1016/j.ecosta.2021.09.008.

Pakbin, Arash, Xiaochen Wang, Bobak J. Mortazavi, and Donald K. K. Lee. 2025. “BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis.” Journal of Statistical Software 113 (3): 1–26. https://doi.org/10.18637/jss.v113.i03.

Parner, Erik T., Per K. Andersen, and Morten Overgaard. 2023. “Regression Models for Censored Time-to-Event Data Using Infinitesimal Jack-Knife Pseudo-Observations, with Applications to Left-Truncation.” Lifetime Data Analysis 29 (3): 654–71. https://doi.org/10.1007/s10985-023-09597-5.

Paszke, Adam, Sam Gross, Soumith Chintala, et al. 2017. Automatic differentiation in pytorch.

Patel, Katie, Richard Kay, and Lucy Rowell. 2006. “Comparing proportional hazards and accelerated failure time models: An application in influenza.” Pharmaceutical Statistics 5 (3): 213–24. https://doi.org/10.1002/pst.213.

Pedregosa, F., G. Varoquaux, A. Gramfort, et al. 2011. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30.

Peto, Richard. 1973. “Experimental Survival Curves for Interval-Censored Observations.” Journal of the Royal Statistical Society. Series C (Applied Statistics) 22 (1): 86–91. https://doi.org/10.2307/2346307.

Peto, Richard, and Julian Peto. 1972. “Asymptotically efficient rank invariant test procedures.” Journal of the Royal Statistical Society: Series A (General) 135 (2): 185–98.

Piller, Johannes, Léa Orsini, Simon Wiegrebe, et al. 2026. “Reduction Techniques for Survival Analysis.” Lifetime Data Analysis 32: 34. https://doi.org/10.1007/s10985-026-09714-0.

Pölsterl, Sebastian. 2020. “scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn.” Journal of Machine Learning Research 21 (212): 1–6. http://jmlr.org/papers/v21/20-729.html.

Pölsterl, Sebastian, Nassir Navab, and Amin Katouzian. 2015. “Fast Training of Support Vector Machines for Survival Analysis.” In Machine Learning and Knowledge Discovery in Databases, edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, and Carlos Soares. Springer International Publishing.

Prince, Simon J. D. 2023. Understanding Deep Learning. MIT Press. http://udlbook.com.

Probst, Philipp, Anne-Laure Boulesteix, and Bernd Bischl. 2019. “Tunability: Importance of Hyperparameters of Machine Learning Algorithms.” Journal of Machine Learning Research 20 (53): 1–32. http://jmlr.org/papers/v20/18-444.html.

Putter, Hein, and Cristian Spitoni. 2018. “Non-Parametric Estimation of Transition Probabilities in Non-Markov Multi-State Models: The Landmark Aalen-Johansen Estimator.” Statistical Methods in Medical Research 27 (7): 2081–92. https://doi.org/10.1177/0962280216674497.

Qi, Jiezhi. 2009. “Comparison of Proportional Hazards and Accelerated Failure Time Models.” PhD thesis.

Qi, Shi-ang, Vahid Balazadeh, Michael Cooper, Russell Greiner, and Rahul G. Krishnan. 2026. SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference. arXiv:2605.15488. arXiv. https://doi.org/10.48550/arXiv.2605.15488.

Qi, Shi-Ang, Neeraj Kumar, Mahtab Farrokh, et al. 2023. “An Effective Meaningful Way to Evaluate Survival Models.” In Proceedings of the 40th International Conference on Machine Learning, edited by Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, vol. 202. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v202/qi23b.html.

R., Cox, and Snell J. 1968. “A General Definition of Residuals.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 30 (2): 248–75.

Rahman, M. Shafiqur, Gareth Ambler, Babak Choodari-Oskooei, and Rumana Z. Omar. 2017. “Review and evaluation of performance measures for survival prediction models in external validation settings.” BMC Medical Research Methodology 17 (1): 1–15. https://doi.org/10.1186/s12874-017-0336-2.

Rahman, Md Mahmudur, Koji Matsuo, Shinya Matsuzaki, and Sanjay Purushotham. 2021. “DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis.” Proceedings of the AAAI Conference on Artificial Intelligence 35: 479–87.

Rahman, Md Mahmudur, and Sanjay Purushotham. 2022. “Pseudo Value-Based Deep Neural Networks for Multi-State Survival Analysis.” Proceedings of the AAAI Conference on Artificial Intelligence (Student Abstract). https://arxiv.org/abs/2207.05291.

Ramachandran, Prajit, Barret Zoph, and Quoc V. Le. 2017. Searching for Activation Functions. https://arxiv.org/abs/1710.05941.

Reid, Nancy. 1994. “A Conversation with Sir David Cox.” Statistical Science 9 (3): 439–55. http://www.jstor.org/stable/2246357.

Ridgeway, Greg. 1999. “The state of boosting.” Computing Science and Statistics 31: 172–81.

Rindt, D, R Hu, D Steinsaltz, and D Sejdinovic. 2022. “Survival Regression with Proper Scoring Rules and Monotonic Neural Networks.” 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), Proceedings of machine learning research, 1190–205.

Rinne, Horst. 2014. The Hazard Rate: Theory and Inference (with Supplementary MATLAB-Programs). Justus-Liebig-University. http://geb.uni-giessen.de/geb/volltexte/2014/10793/.

Robbins, Herbert, and Sutton Monro. 1951. “A Stochastic Approximation Method.” The Annals of Mathematical Statistics 22 (3): 400–407. https://doi.org/10.1214/aoms/1177729586.

Robins, James M., and Dianne M. Finkelstein. 2000. “Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests.” Biometrics 56 (3): 779–88. https://doi.org/10.1111/j.0006-341X.2000.00779.x.

Royston, Patrick, and Douglas G. Altman. 2013. “External validation of a Cox prognostic model: Principles and methods.” BMC Medical Research Methodology 13 (1). https://doi.org/10.1186/1471-2288-13-33.

Royston, Patrick, and Mahesh K. B. Parmar. 2002. “Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.” Statistics in Medicine 21 (15): 2175–97. https://doi.org/10.1002/sim.1203.

Royston, Patrick, and Mahesh K. B. Parmar. 2011a. “The Use of Restricted Mean Survival Time to Estimate the Treatment Effect in Randomized Clinical Trials When the Proportional Hazards Assumption Is in Doubt.” Statistics in Medicine 30 (19): 2359–71. https://doi.org/10.1002/sim.4274.

Royston, Patrick, and Mahesh K. B. Parmar. 2011b. “The Use of Restricted Mean Survival Time to Estimate the Treatment Effect in Randomized Clinical Trials When the Proportional Hazards Assumption Is in Doubt.” Statistics in Medicine 30 (19): 2409–21. https://doi.org/10.1002/sim.4274.

Royston, Patrick, and Willi Sauerbrei. 2004. “A new measure of prognostic separation in survival data.” Statistics in Medicine 23 (5): 723–48. https://doi.org/10.1002/sim.1621.

Rubio, Francisco Javier, and Reza Drikvandi. 2022. “MEGH: A Parametric Class of General Hazard Models for Clustered Survival Data.” Statistical Methods in Medical Research 31 (8): 1603–16. https://doi.org/10.1177/09622802221102620.

Rubio, Francisco J, Laurent Remontet, Nicholas P Jewell, and Aurélien Belot. 2019. “On a General Structure for Hazard-Based Regression Models: An Application to Population-Based Cancer Research.” Statistical Methods in Medical Research 28 (8): 2404–17. https://doi.org/10.1177/0962280218782293.

Rügamer, David, Chris Kolb, and Nadja Klein. 2024. “Semi-Structured Distributional Regression.” The American Statistician 78 (1): 88–99. https://doi.org/10.1080/00031305.2022.2164054.

Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986. “Learning representations by back-propagating errors.” Nature 323 (6088): 533–36. https://doi.org/10.1038/323533a0.

Sachs, Michael C., and Erin E. Gabriel. 2022. “Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R.” Journal of Statistical Software 102 (9): 1–34. https://doi.org/10.18637/jss.v102.i09.

Saha, P., and P. J. Heagerty. 2010. “Time-Dependent Predictive Accuracy in the Presence of Competing Risks.” Biometrics 66 (4): 999–1011. https://doi.org/10.1111/j.1541-0420.2009.01375.x.

Sashegyi, Andreas, and David Ferry. 2017. “On the Interpretation of the Hazard Ratio and Communication of Survival Benefit.” The Oncologist 22 (4): 484–86. https://doi.org/10.1634/theoncologist.2016-0198.

Schemper, Michael, and Robin Henderson. 2000. “Predictive Accuracy and Explained Variation in Cox Regression.” Biometrics 56: 249–55. https://doi.org/10.1002/sim.1486.

Schenk, Alina, Vanessa Basten, and Matthias Schmid. 2025. “Modeling the Restricted Mean Survival Time Using Pseudo-Value Random Forests.” Statistics in Medicine 44 (5): e70031. https://doi.org/10.1002/sim.70031.

Schmid, Matthias, Thomas Hielscher, Thomas Augustin, and Olaf Gefeller. 2011. “A Robust Alternative to the Schemper-Henderson Estimator of Prediction Error.” Biometrics 67 (2): 524–35. https://doi.org/10.1111/j.1541-0420.2010.01459.x.

Schmid, Matthias, and Torsten Hothorn. 2008a. “Boosting additive models using component-wise P-splines.” Computational Statistics & Data Analysis 53 (2): 298–311. https://doi.org/10.1016/j.csda.2008.09.009.

Schmid, Matthias, and Torsten Hothorn. 2008b. “Flexible boosting of accelerated failure time models.” BMC Bioinformatics 9 (February): 269. https://doi.org/10.1186/1471-2105-9-269.

Schmid, Matthias, and Sergej Potapov. 2012. “A comparison of estimators to evaluate the discriminatory power of time-to-event models.” Statistics in Medicine 31 (23): 2588–609. https://doi.org/10.1002/sim.5464.

Schmid, Matthias, Marvin Wright, and Andreas Ziegler. 2016. “On the Use of Harrell’s c for Clinical Risk Prediction via Random Survival Forests.” Expert Systems with Applications 63 (July). https://doi.org/10.1016/j.eswa.2016.07.018.

Schoop, Rotraut, Jan Beyersmann, Martin Schumacher, and Harald Binder. 2011. “Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.” Biometrical Journal 53 (1): 88–112. https://doi.org/10.1002/bimj.201000073.

Schoop, Rotraut, Martin Schumacher, and Erika Graf. 2011. “Measures of prediction error for survival data with longitudinal covariates.” Biometrical Journal 53 (2): 275–93. https://doi.org/10.1002/bimj.201000145.

Schulz-Kümpel, Hannah, Sebastian Fischer, Roman Hornung, Anne-Laure Boulesteix, Thomas Nagler, and Bernd Bischl. 2025. “Constructing Confidence Intervals for ‘the’ Generalization Error – a Comprehensive Benchmark Study.” Journal of Data-Centric Machine Learning Research 2. https://data.mlr.press/assets/pdf/v02-6.pdf.

Schwarzer, Guido, Werner Vach, and Martin Schumacher. 2000. “On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology.” Statistics in Medicine 19 (4): 541–61. https://doi.org/10.1002/(SICI)1097-0258(20000229)19:4<541::AID-SIM355>3.0.CO;2-V.

Segal, Mark Robert. 1988. “Regression Trees for Censored Data.” Biometrics 44 (1): 35–47.

Seletkov, Dmitrii, Paul Hager, Georgios Kaissis, Rickmer Braren, Daniel Rueckert, and Raphael Rehms. 2026. Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks. arXiv:2603.29475. arXiv. https://doi.org/10.48550/arXiv.2603.29475.

Shivaswamy, Pannagadatta K., Wei Chu, and Martin Jansche. 2007. “A support vector approach to censored targets.” Proceedings - IEEE International Conference on Data Mining, ICDM, 655–60. https://doi.org/10.1109/ICDM.2007.93.

Silverman, B. W. 1985. “Some aspects of the spline smoothing approach to non-parametric regression curve fitting.” Journal of the Royal Statistical Society. Series B (Methodological) 47 (1): 1–52.

Simon, Noah, Jerome H. Friedman, Trevor Hastie, and Rob Tibshirani. 2011. “Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent.” Journal of Statistical Software 39 (5): 1–13. https://doi.org/10.18637/jss.v039.i05.

Simon, Richard. 2007. “Resampling Strategies for Model Assessment and Selection.” In Fundamentals of Data Mining in Genomics and Proteomics, edited by Werner Dubitzky, Martin Granzow, and Daniel Berrar. Springer US. https://doi.org/10.1007/978-0-387-47509-7_8.

Simonsen, Mikkel Runason, and Rasmus Plenge Waagepetersen. 2025. “A-Calibration: Assessment of Prediction Models for Survival Data Under Censoring.” BMC Medical Research Methodology 25 (236). https://doi.org/10.1186/s12874-025-02671-6.

Sonabend, Raphael, Andreas Bender, and Sebastian Vollmer. 2022. “Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.” Bioinformatics 38 (17): 4178–84. https://doi.org/10.1093/bioinformatics/btac451.

Sonabend, Raphael, Florian Pfisterer, Alan Mishler, et al. 2022. “Flexible Group Fairness Metrics for Survival Analysis.” DSHealth 2022 Workshop on Applied Data Science for Healthcare at KDD2022. http://arxiv.org/abs/2206.03256.

Sonabend, Raphael, John Zobolas, Riccardo Be Bin, Philipp Kopper, Lukas Burk, and Andreas Bender. 2025. Examining Marginal Properness in the External Validation of Survival Models with Squared and Logarithmic Losses. https://arxiv.org/abs/2212.05260.

Song, Xiao, and Xiao-Hua Zhou. 2008. “A semiparametric approach for the covariate specific ROC curve with survival outcome.” Statistica Sinica 18 (July): 947–65. http://www.jstor.org/stable/24308524.

Sparapani, Rodney A., Brent R. Logan, Robert E. McCulloch, and Purushottam W. Laud. 2016. “Nonparametric Survival Analysis Using Bayesian Additive Regression Trees (BART).” Statistics in Medicine 35 (16): 2741–53. https://doi.org/10.1002/sim.6893.

Spooner, Annette, Emily Chen, Arcot Sowmya, et al. 2020. “A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction.” Scientific Reports 10 (1): 20410. https://doi.org/10.1038/s41598-020-77220-w.

Spruance, Spotswood L, Julia E Reid, Michael Grace, and Matthew Samore. 2004. “Hazard ratio in clinical trials.” Antimicrobial Agents and Chemotherapy 48 (8): 2787–92. https://doi.org/10.1128/AAC.48.8.2787-2792.2004.

Srivastava, Nitish, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. “Dropout: a simple way to prevent neural networks from overfitting.” The Journal of Machine Learning Research 15 (1): 1929–58.

Suissa, Samy. 2008. “Immortal Time Bias in Pharmacoepidemiology.” American Journal of Epidemiology 167 (4): 492–99. https://doi.org/10.1093/aje/kwm324.

Sun, Jianguo. 2006. The Statistical Analysis of Interval-Censored Failure Time Data. Springer. https://doi.org/10.1007/0-387-37119-2.

Therneau, Terry M. 2024. A Package for Survival Analysis in r. https://CRAN.R-project.org/package=survival.

Therneau, Terry M., and Elizabeth Atkinson. 2024. Concordance. https://cran.r-project.org/web/packages/survival/vignettes/concordance.pdf.

Therneau, Terry M., and Patricia M. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. New York.

Therneau, Terry M., and Patricia M. Grambsch. 2001. Modeling Survival Data: Extending the Cox Model. 1st ed. 2000. Corr. 2nd printing 2001. Springer.

Tian, Lu, Lihui Zhao, and L. J. Wei. 2014. “Predicting the Restricted Mean Event Time with the Subject’s Baseline Covariates in Survival Analysis.” Biometrics 70 (3): 717–25. https://doi.org/10.1093/biostatistics/kxt050.

Tibshirani, Robert. 1997. “The Lasso Method for Variable Selection in the Cox Model.” Statistics in Medicine 16 (4): 385–95. https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3.

Tong, Xingwei, Liang Zhu, Chenlei Leng, Wendy Leisenring, and Leslie L. Robison. 2013. “A General Semiparametric Hazards Regression Model: Efficient Estimation and Structure Selection.” Statistics in Medicine 32 (28): 4980–94. https://doi.org/10.1002/sim.5885.

Tsouprou, Sofia. 2015. “Measures of Discrimination and Predictive Accuracy for Interval Censored Survival Data.” Master's thesis.

Turnbull, Bruce W. 1974. “Nonparametric Estimation of a Survivorship Function with Doubly Censored Data.” Journal of the American Statistical Association 69 (345): 169–73. https://doi.org/10.1080/01621459.1974.10480146.

Turnbull, Bruce W. 1976. “The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data.” Journal of the Royal Statistical Society. Series B (Methodological) 38 (3): 290–95. https://doi.org/10.1111/j.2517-6161.1976.tb01597.x.

Tutz, Gerhard, and Harald Binder. 2007. “Boosting Ridge Regression.” Computational Statistics & Data Analysis 51 (February): 6044–59. https://doi.org/10.1016/j.csda.2006.11.041.

Tutz, Gerhard, and Matthias Schmid. 2016. Modeling Discrete Time-to-Event Data. Springer Series in Statistics. Springer International Publishing. https://doi.org/10.1007/978-3-319-28158-2.

Uno, Hajime, Tianxi Cai, Michael J. Pencina, Ralph B. D’Agostino, and L J Wei. 2011. “On the C-statistics for Evaluating Overall Adequacy of Risk Prediction Procedures with Censored Survival Data.” Statistics in Medicine 30 (10): 1105–17. https://doi.org/10.1002/sim.4154.

Uno, Hajime, Tianxi Cai, Lu Tian, and L J Wei. 2007. “Evaluating Prediction Rules for t-Year Survivors with Censored Regression Models.” Journal of the American Statistical Association 102 (478): 527–37. http://www.jstor.org/stable/27639883.

Uno, Hajime, Brian Claggett, Lu Tian, et al. 2014. “Moving Beyond the Hazard Ratio in Quantifying the Between-Group Difference in Survival Analysis.” Journal of Clinical Oncology 32 (22): 2380–85. https://doi.org/10.1200/JCO.2014.55.2208.

Vakulenko-Lagun, Bella, Micha Mandel, and Rebecca A. Betensky. 2020. “Inverse Probability Weighting Methods for Cox Regression with Right-Truncated Data.” Biometrics 76 (2): 484–95. https://doi.org/10.1111/biom.13162.

Van Belle, Vanya, Kristiaan Pelckmans, Johan A K Suykens, and Sabine Van Huffel. 2008. “Survival SVM: a practical scalable algorithm.” Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN), 89–94.

Van Belle, Vanya, Kristiaan Pelckmans, Johan A. K. Suykens, and Sabine Van Huffel. 2007. “Support Vector Machines for Survival Analysis.” In Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare. https://ftp.esat.kuleuven.be/pub/stadius/vanbelle/reports/07-70.pdf.

Van Belle, Vanya, Kristiaan Pelckmans, Sabine Van Huffel, and Johan A. K. Suykens. 2011. “Support vector methods for survival analysis: A comparison between ranking and regression approaches.” Artificial Intelligence in Medicine 53 (2): 107–18. https://doi.org/10.1016/j.artmed.2011.06.006.

Van Houwelingen, Hans C. 2000. “Validation, calibration, revision and combination of prognostic survival models.” Statistics in Medicine 19 (24): 3401–15. https://doi.org/10.1002/1097-0258(20001230)19:24<3401::AID-SIM554>3.0.CO;2-2.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. 2017. “Attention is all you need.” Advances in Neural Information Processing Systems 30: 5998–6008.

Vock, David M, Julian Wolfson, Sunayan Bandyopadhyay, et al. 2016. “Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.” Journal of Biomedical Informatics 61: 119–31. https://doi.org/10.1016/j.jbi.2016.03.009.

Volinsky, Chris T, and Adrian E Raftery. 2000. “Bayesian Information Criterion for Censored Survival Models.” International Biometric Society 56 (1): 256–62.

Wang, Ping, Yan Li, and Chandan K. Reddy. 2019. “Machine Learning for Survival Analysis.” ACM Computing Surveys 51 (6): 1–36. https://doi.org/10.1145/3214306.

Wang, Xiaochen, Arash Pakbin, Bobak J. Mortazavi, Hongyu Zhao, and Donald K. K. Lee. 2020. “BoXHED: Boosted eXact Hazard Estimator with Dynamic Covariates.” Proceedings of the 37th International Conference on Machine Learning, Proceedings of machine learning research, vol. 119: 9973–82. https://proceedings.mlr.press/v119/wang20o.html.

Wang, Zhu, and C Y Wang. 2010. “Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data.” Statistical Applications in Genetics and Molecular Biology (Berlin, Boston) 9 (1). https://doi.org/10.2202/1544-6115.1550.

Wei, L J. 1992. “The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis.” Statistics in Medicine 11: 1871–79.

Wiegrebe, Simon, Philipp Kopper, Raphael Sonabend, Bernd Bischl, and Andreas Bender. 2024. “Deep learning for survival analysis: a review.” Artificial Intelligence Review 57 (3): 65. https://doi.org/10.1007/s10462-023-10681-3.

Wilks, Daniel S. 1990. “On the Combination of Forecast Probabilities for Consecutive Precipitation Periods.” Weather and Forecasting (Boston MA, USA) 5 (4): 640–50. https://doi.org/10.1175/1520-0434(1990)005<0640:OTCOFP>2.0.CO;2.

Willems, SJW, A Schat, MS van Noorden, and M Fiocco. 2018. “Correcting for Dependent Censoring in Routine Outcome Monitoring Data by Applying the Inverse Probability Censoring Weighted Estimator.” Statistical Methods in Medical Research 27 (2): 323–35. https://doi.org/10.1177/0962280216628900.

Wolbers, Marcel, Paul Blanche, Michael T. Koller, Jacqueline C. M. Witteman, and Thomas A. Gerds. 2014. “Concordance for Prognostic Models with Competing Risks.” Biostatistics 15 (3): 526–39. https://doi.org/10.1093/biostatistics/kxt059.

Wolbers, Marcel, Michael T Koller, Jacqueline C M Witteman, and Ewout W Steyerberg. 2009. “Prognostic Models With Competing Risks: Methods and Application to Coronary Risk Prediction.” Epidemiology 20 (4). https://journals.lww.com/epidem/fulltext/2009/07000/prognostic_models_with_competing_risks__methods.14.aspx.

Wood, Simon N. 2003. “Thin Plate Regression Splines.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65 (1): 95–114. https://doi.org/10.1111/1467-9868.00374.

Wood, Simon N. 2017. Generalized Additive Models: An Introduction with R. 2nd ed. Chapman; Hall/CRC. https://doi.org/10.1201/9781315370279.

Wright, Marvin N., and Andreas Ziegler. 2017. “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software 77 (1): 1–17.

Wu, Lingfei, Peng Cui, Jian Pei, and Liang Zhao. 2022. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer Singapore.

Wu, Ying, and Richard J Cook. 2020. “Assessing the Accuracy of Predictive Models with Interval-Censored Data.” Biostatistics 23 (1): 18–33. https://doi.org/10.1093/biostatistics/kxaa011.

Wu, Yuan, Xiaofei Wang, Jiaxing Lin, Beilin Jia, and Kouros Owzar. 2020. “Predictive Accuracy of Markers or Risk Scores for Interval Censored Survival Data.” Statistics in Medicine 39 (18): 2437–46. https://doi.org/10.1002/sim.8547.

Yanagisawa, Hiroki. 2023. “Proper Scoring Rules for Survival Analysis.” In Proceedings of the 40th International Conference on Machine Learning, edited by Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, vol. 202. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v202/yanagisawa23a.html.

Zare, Ali, Mostafa Hosseini, Mahmood Mahmoodi, Kazem Mohammad, Hojjat Zeraati, and Kourosh Holakouie Naieni. 2015. “A Comparison between Accelerated Failure-time and Cox Proportional Hazard Models in Analyzing the Survival of Gastric Cancer Patients.” Iranian Journal of Public Health 44 (8): 1095–102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4645729/.

Zhang, Yunwei, Germaine Wong, Graham Mann, Samuel Muller, and Jean Y H Yang. 2022. “SurvBenchmark: Comprehensive Benchmarking Study of Survival Analysis Methods Using Both Omics Data and Clinical Data.” GigaScience 11 (January): giac071. https://doi.org/10.1093/gigascience/giac071.

Zhang, Zhigang, and Jianguo Sun. 2010. “Interval Censoring.” Statistical Methods in Medical Research 19 (1): 53–70. https://doi.org/10.1177/0962280209105023.

Zhao, Lianhe, Qiongye Dong, Chunlong Luo, et al. 2021. “DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis.” Computational and Structural Biotechnology Journal 19: 2719–25. https://doi.org/10.1016/j.csbj.2021.04.067.

Zhao, Lili, and Dai Feng. 2020a. “Deep Neural Networks for Survival Analysis Using Pseudo Values.” IEEE Journal of Biomedical and Health Informatics 24 (11): 3308–14. https://doi.org/10.1109/JBHI.2020.2980204.

Zhao, Lili, and Dai Feng. 2020b. “Deep Neural Networks for Survival Analysis Using Pseudo Values.” IEEE Journal of Biomedical and Health Informatics 24 (11): 3308–14. https://doi.org/10.1109/JBHI.2020.2980204.

Zheng, Yingye, Tianxi Cai, Yuying Jin, and Ziding Feng. 2012. “Evaluating Prognostic Accuracy of Biomarkers Under Competing Risk.” Biometrics 68 (2): 388–96. https://doi.org/10.1111/j.1541-0420.2011.01671.x.

Zhu, X, J Yao, and J Huang. 2016. “Deep convolutional neural network for survival analysis with pathological images.” 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 544–47. https://doi.org/10.1109/BIBM.2016.7822579.

Zou, Hui, and Trevor Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society Series B: Statistical Methodology 67 (2): 301–20. https://doi.org/10.1111/j.1467-9868.2005.00503.x.