References

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

Abner, Erin L, Richard J Charnigo, and Richard J Kryscio. 2013. “Markov chains and semi-Markov models in time-to-event analysis.” Journal of Biometrics & Biostatistics Suppl 1 (e001): 19522. https://doi.org/10.4172/2155-6180.S1-e001.

Akaike, Hirotugu. 1974. “A New Look at the Statistical Model Identification.” IEEE Transactions on Automatic Control 19 (6): 716–23. https://doi.org/10.1093/ietfec/e90-a.12.2762.

Akritas, Michael G. 1994. “Nearest Neighbor Estimation of a Bivariate Distribution Under Random Censoring.” Ann. Statist. 22 (3): 1299–1327. https://doi.org/10.1214/aos/1176325630.

Andres, Axel, Aldo Montano-Loza, Russell Greiner, Max Uhlich, Ping Jin, Bret Hoehn, David Bigam, James Andrew Mark Shapiro, and Norman Mark Kneteman. 2018. “A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis.” PLOS ONE 13 (3): e0193523. https://doi.org/10.1371/journal.pone.0193523.

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.

Avati, Anand, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, and Andrew Ng. 2020. “Countdown Regression: Sharp and Calibrated Survival Predictions.” In Proceedings of Machine Learning Research, 145—–155. https://proceedings.mlr.press/v115/avati20a.html http://arxiv.org/abs/1806.08324.

Bakker, Bart, Tom Heskes, Jan Neijt, and Bert Kappen. 2004. “Improving Cox survival analysis with a neural-Bayesian approach.” Statistics in Medicine 23 (19): 2989–3012. https://doi.org/10.1002/sim.1904.

Becker, Marc, Lennart Schneider, and Sebastian Fischer. 2024. “Hyperparameter Optimization.” 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/hyperparameter_optimization.html.

Bello, Ghalib A, Timothy J W Dawes, Jinming Duan, Carlo Biffi, Antonio de Marvao, Luke S G E Howard, J Simon R Gibbs, et al. 2019. “Deep-learning cardiac motion analysis for human survival prediction.” Nature Machine Intelligence 1 (2): 95–104. https://doi.org/10.1038/s42256-019-0019-2.

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

Biganzoli, E M, F Ambrogi, and P Boracchi. 2009. “Partial logistic artificial neural networks (PLANN) for flexible modeling of censored survival data.” In 2009 International Joint Conference on Neural Networks, 340–46. https://doi.org/10.1109/IJCNN.2009.5178824.

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, 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.

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

Binder, Martin, Florian Pfisterer, Bernd Bischl, Michel Lang, and Susanne Dandl. 2019. “mlr3pipelines: Preprocessing Operators and Pipelines for ’mlr3’.” CRAN. https://cran.r-project.org/package=mlr3pipelines.

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.

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, Therese M.-L. Andersson, Mark Clements, Xing-Rong Liu, Paul W Dickman, and Paul C Lambert. 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.

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

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.

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.

Chen, Tianqi, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, et al. 2020. “xgboost: Extreme Gradient Boosting.” CRAN. https://cran.r-project.org/package=xgboost.

Chen, Yen-Chen, Wan-Chi Ke, and Hung-Wen Chiu. 2014. “Risk classification of cancer survival using ANN with gene expression data from multiple laboratories.” Computers in Biology and Medicine 48: 1–7. https://doi.org/https://doi.org/10.1016/j.compbiomed.2014.02.006.

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

Ching, Travers. 2015. “Cox-Nnet.” https://github.com/lanagarmire/cox-nnet.

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.

Choodari-Oskooei, Babak, Patrick Royston, and Mahesh K. B. Parmar. 2012. “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.

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/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.

Clevert, Djork-Arné, Thomas Unterthiner, and Sepp Hochreiter. 2015. “Fast and accurate deep network learning by exponential linear units (elus).” arXiv Preprint arXiv:1511.07289.

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

Collins, Gary S., Joris A. De Groot, Susan Dutton, Omar Omar, Milensu Shanyinde, Abdelouahid Tajar, Merryn Voysey, et al. 2014. “External validation of multivariable prediction models: A systematic review of methodological conduct and reporting.” BMC Medical Research Methodology 14 (1): 1–11. https://doi.org/10.1186/1471-2288-14-40.

Colosimo, Enrico, Fla´vio Ferreira, Maristela Oliveira, and Cleide Sousa. 2002. “Empirical comparisons between Kaplan-Meier and Nelson-Aalen survival function estimators.” Journal of Statistical Computation and Simulation 72 (4): 299–308. https://doi.org/10.1080/00949650212847.

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.

———. 1975. “Partial Likelihood.” Biometrika 62 (2): 269–76. https://doi.org/10.1080/03610910701884021.

Cui, Lei, Hansheng Li, Wenli Hui, Sitong Chen, Lin Yang, Yuxin Kang, Qirong Bo, and Jun Feng. 2020. “A deep learning-based framework for lung cancer survival analysis with biomarker interpretation.” BMC Bioinformatics 21 (1): 112. https://doi.org/10.1186/s12859-020-3431-z.

Data Study Group Team. 2020. “Data Study Group Final Report: Great Ormond Street Hospital.” https://doi.org/10.5281/zenodo.3670726.

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—–218.

Dawid, A Philip, and Monica Musio. 2014. “Theory and Applications of Proper Scoring Rules.” Metron 72 (2): 169–83. https://arxiv.org/abs/arXiv:1401.0398v1.

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 (Jan): 1–30.

Dietterich, Thomas G. 1998. “Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms.” Neural Computation 10 (7): 1895–1923. https://doi.org/10.1162/089976698300017197.

Du, Xian, and Sumeet Dua. 2011. “Cancer prognosis using support vector regression in imaging modality.” World Journal of Clinical Oncology 2 (1): 44–49. https://doi.org/10.5306/wjco.v2.i1.44.

Efron, Bradley. 1988. “Logistic Regression, Survival Analysis, and the Kaplan-Meier Curve.” Journal of the American Statistical Association 83 (402): 414–25. https://doi.org/10.2307/2288857.

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

Faraggi, David, and Richard Simon. 1995. “A neural network model for survival data.” Statistics in Medicine 14 (1): 73–82. https://doi.org/10.1002/sim.4780140108.

Faraggi, David, R Simon, E Yaskil, and A Kramar. 1997. “Bayesian Neural Network Models for Censored Data.” Biometrical Journal 39 (5): 519–32. https://doi.org/10.1002/bimj.4710390502.

Fernández, Tamara, Nicolas Nicolás Rivera, and Yee Whye Teh. 2016. “Gaussian Processes for Survival Analysis.” In Advances in Neural Information Processing Systems. Vol. 29. Nips. Curran Associates, Inc. http://arxiv.org/abs/1611.00817 https://proceedings.neurips.cc/paper/2016/file/ef1e491a766ce3127556063d49bc2f98-Paper.pdf.

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.

Foss, Natalie, and Lars Kotthoff. 2024. “Data and Basic Modeling.” 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/data_and_basic_modeling.html.

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.

Freund, Yoav, and Robert E Schapire. 1996. “Experiments with a new boosting algorithm.” In. Citeseer.

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): 1189–1232. 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.

Fritsch, Stefan, Frauke Guenther, and Marvin N. Wright. 2019. “neuralnet: Training of Neural Networks.” CRAN. https://cran.r-project.org/package=neuralnet.

Gelfand, Alan E, Sujit K Ghosh, Cindy Christiansen, Stephen B Soumerai, and Thomas J McLaughlin. 2000. “Proportional hazards models: a latent competing risk approach.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 49 (3): 385–97. https://doi.org/https://doi.org/10.1111/1467-9876.00199.

Gensheimer, Michael F., and Balasubramanian Narasimhan. 2018. “A Simple Discrete-Time Survival Model for Neural Networks,” 1–17. https://doi.org/arXiv:1805.00917v3.

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

Georgousopoulou, Ekavi N, Christos Pitsavos, Christos Mary Yannakoulia, and Demosthenes B Panagiotakos. 2015. “Comparisons between Survival Models in Predicting Cardiovascular Disease Events : Application in the ATTICA Study ( 2002-2012 ).” Journal of Statistics Applications & Probability 4 (2): 203–10.

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.

Giunchiglia, Eleonora, Anton Nemchenko, and Mihaela van der Schaar. 2018. “Rnn-surv: A deep recurrent model for survival analysis.” In International Conference on Artificial Neural Networks, 23–32. Springer.

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.” Edited by Francesco Pappalardo. 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.

Gönen, Mithat, and Glenn Heller. 2005. “Concordance Probability and Discriminatory Power in Proportional Hazards Regression.” Biometrika 92 (4): 965–70.

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.

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.

Greenwell, Brandon, Bradley Boehmke, Jay Cunningham, and. GBM Developers. 2019. “gbm: Generalized Boosted Regression Models.” CRAN. https://cran.r-project.org/package=gbm.

Gressmann, Frithjof, Franz J. Király, Bilal Mateen, and Harald Oberhauser. 2018. “Probabilistic supervised learning.” https://doi.org/10.1002/iub.552.

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, Ilkyu, June Hyuk Kim, Heeseol Park, Han-Soo Kim, and Sung Wook Seo. 2018. “Deep learning approach for survival prediction for patients with synovial sarcoma.” Tumor Biology 40 (9): 1010428318799264. https://doi.org/10.1177/1010428318799264.

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. http://dx.doi.org/10.1001/jama.1982.03320430047030.

Harrell, Frank E., Kerry L. Lee, and Daniel B. Mark. 1996. “Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors.” Statistics in Medicine 15: 361–87. https://doi.org/10.1002/0470023678.ch2b(i).

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.

Henderson, and Velleman. 1981. “Building multiple regression models interactively.” Biometrics 37: 391—–411.

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.” In, edited by Andreas Fink, Berthold Lausen, Wilfried Seidel, and Alfred Ultsch, 663–73. Berlin, Heidelberg: Springer Berlin Heidelberg.

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, 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, Kurt Hornik, and Achim Zeileis. 2006. “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics 15 (3): 651—–674.

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.

Hothorn, Torsten, and Achim Zeileis. 2015. “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research 16: 3905–9. http://jmlr.org/papers/v16/hothorn15a.html.

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

———. 2020b. “Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.” Cancer Letters 471: 61–71. https://doi.org/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. 1979. “Comparison of Four Tests for Equality of Survival Curves in the Presence of Stratification and Censoring.” Biometrika 66 (3): 419–28. https://doi.org/10.1093/biomet/76.2.297.

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

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

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

Jackson, Dan, Ian R. White, Shaun Seaman, Hannah Evans, Kathy Baisley, and James Carpenter. 2014. “Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation.” Statistics in Medicine 33 (27): 4681–94. https://doi.org/10.1002/sim.6274.

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/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. New York: Springer.

Jing, Bingzhong, Tao Zhang, Zixian Wang, Ying Jin, Kuiyuan Liu, Wenze Qiu, Liangru Ke, et al. 2018. “RankDeepSurv.” https://github.com/sysucc-ailab/RankDeepSurv.

———, et al. 2019. “A deep survival analysis method based on ranking.” Artificial Intelligence in Medicine 98: 1–9. https://doi.org/https://doi.org/10.1016/j.artmed.2019.06.001.

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. 2011. The statistical analysis of failure time data. Vol. 360. 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.

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.

Katzman, Jared, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. 2016. “Deep Survival: A Deep Cox Proportional Hazards Network,” June.

Kent, John T., and John O’Quigley. 1988. “Measures of dependence for censored survival data.” Biometrika 75 (3): 525–34. https://doi.org/10.1093/biomet/75.3.525.

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, Minyoung, and Vladimir Pavlovic. 2018. “Variational Inference for Gaussian Process Models for Survival Analysis.” UAI, 435–45.

Király, Franz J., Markus Löning, Anthony Blaom, Ahmed Guecioueur, and Raphael Sonabend. 2021. “Designing Machine Learning Toolboxes: Concepts, Principles and Patterns.” arXiv, January. http://arxiv.org/abs/2101.04938.

Király, Franz J, Bilal Mateen, and Raphael Sonabend. 2018. “NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature.” arXiv, December. http://arxiv.org/abs/1812.07519.

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.

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

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.

Kvamme, Håvard. 2018. “Pycox.” https://pypi.org/project/pycox/.

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.

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/https://doi.org/10.1016/j.procs.2011.08.050.

Langford, John, Paul Mineiro, Alina Beygelzimer, and Hal Daume. 2016. “Learning Reductions that Really Work.” Proceedings of the IEEE 104 (1).

Lao, Jiangwei, Yinsheng Chen, Zhi-Cheng Li, Qihua Li, Ji Zhang, Jing Liu, and Guangtao Zhai. 2017. “A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.” Scientific Reports 7 (1): 10353. https://doi.org/10.1038/s41598-017-10649-8.

Lawless, Jerald F, and Yan Yuan. 2010. “Estimation of prediction error for survival models.” Statistics in Medicine 29 (2): 262–74. https://doi.org/10.1002/sim.3758.

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

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

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. 2019. “Boosted nonparametric hazards with time-dependent covariates.” https://arxiv.org/abs/arXiv:1701.07926v6.

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.

Liestol, Knut, Per Kragh Andersen, and Ulrich Andersen. 1994. “Survival analysis and neural nets.” Statistics in Medicine 13 (12): 1189–1200. https://doi.org/10.1002/sim.4780131202.

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

Lundin, M, J Lundin, H B Burke, S Toikkanen, L Pylkkänen, and H Joensuu. 1999. “Artificial Neural Networks Applied to Survival Prediction in Breast Cancer.” Oncology 57 (4): 281–86. https://doi.org/10.1159/000012061.

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.

Mani, D R, James Drew, Andrew Betz, and Piew Datta. 1999. “Statistics and data mining techniques for lifetime value modeling.” In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 94–103.

Mariani, L, D Coradini, E Biganzoli, P Boracchi, E Marubini, S Pilotti, B Salvadori, et al. 1997. “Prognostic factors for metachronous contralateral breast cancer: A comparison of the linear Cox regression model and its artificial neural network extension.” Breast Cancer Research and Treatment 44 (2): 167–78. https://doi.org/10.1023/A:1005765403093.

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.

McKinney, Scott Mayer, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, et al. 2020. “International evaluation of an AI system for breast cancer screening.” Nature 577 (7788): 89–94. https://doi.org/10.1038/s41586-019-1799-6.

Meinshausen, Nicolai, and Peter Bühlmann. 2010. “Stability selection.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72 (4): 417–73. https://doi.org/10.1111/j.1467-9868.2010.00740.x.

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/.

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

N. Venables, W, and B D. Ripley. 2002. Modern Applied Statistics with S. Springer. http://www.stats.ox.ac.uk/pub/MASS4.

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

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

Nasejje, Justine B, Henry Mwambi, Keertan Dheda, and Maia Lesosky. 2017. “A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.” BMC Medical Research Methodology 17 (1): 115. https://doi.org/10.1186/s12874-017-0383-8.

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

Nezhad, Milad Zafar, Najibesadat Sadati, Kai Yang, and Dongxiao Zhu. 2019. “A Deep Active Survival Analysis approach for precision treatment recommendations: Application of prostate cancer.” Expert Systems with Applications 115: 16–26. https://doi.org/https://doi.org/10.1016/j.eswa.2018.07.070.

Ng, Ryan, Kathy Kornas, Rinku Sutradhar, Walter P. Wodchis, and Laura C. Rosella. 2018. “The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review.” Diagnostic and Prognostic Research 2 (1): 4. https://doi.org/10.1186/s41512-018-0026-5.

Oh, Sung Eun, Sung Wook Seo, Min-Gew Choi, Tae Sung Sohn, Jae Moon Bae, and Sung Kim. 2018. “Prediction of Overall Survival and Novel Classification of Patients with Gastric Cancer Using the Survival Recurrent Network.” Annals of Surgical Oncology 25 (5): 1153–59. https://doi.org/10.1245/s10434-018-6343-7.

Ohno-Machado, Lucila. 1996. “Medical applications of artificial neural networks: connectionist models of survival.” Stanford University Stanford, Calif.

———. 1997. “A COMPARISON OF COX PROPORTIONAL HAZARDS AND ARTIFICIAL NEURAL NETWORK MODELS FOR MEDICAL PROGNOSIS The theoretical advantages and disadvantages of using different methods for predicting survival have seldom been tested in real data sets [ 1 , 2 ]. Althou.” Comput. Biol. Med 27 (1): 55–65.

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.

Peters, Andrea, and Torsten Hothorn. 2019. “ipred: Improved Predictors.” CRAN. https://cran.r-project.org/package=ipred.

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.

Puddu, Paolo Emilio, and Alessandro Menotti. 2012. “Artificial neural networks versus proportional hazards Cox models to predict 45-year all-cause mortality in the Italian Rural Areas of the Seven Countries Study.” BMC Medical Research Methodology 12 (1): 100. https://doi.org/10.1186/1471-2288-12-100.

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

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.

Rasmussen, C. E., and C. K. I. Williams. 2004. Gaussian processes for machine learning. Vol. 14. 2. https://doi.org/10.1142/S0129065704001899.

Reid, Nancy. 1994. “A Conversation with Sir David Cox.” Statistical Science 9 (3): 439–55. https://doi.org/10.1214/aos/1176348654.

Ridgeway, Greg. 1999. “The state of boosting.” Computing Science and Statistics 31: 172—–181.

Rietschel, Carl, Jinsung Yoon, and Mihaela van der Schaar. 2018. “Feature Selection for Survival Analysis with Competing Risks using Deep Learning.” arXiv Preprint arXiv:1811.09317.

Rindt, David, Robert Hu, David Steinsaltz, and Dino Sejdinovic. 2022. “Survival Regression with Proper Scoring Rules and Monotonic Neural Networks,” March. http://arxiv.org/abs/2103.14755.

Ripley, Brian D, and Ruth M Ripley. 2001. “Neural networks as statistical methods in survival analysis.” In Clinical Applications of Artificial Neural Networks, edited by Richard Dybowski and Vanya Gant, 237–55. Cambridge: Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9780511543494.011.

Ripley, R M, A L Harris, and L Tarassenko. 1998. “Neural network models for breast cancer prognosis.” Neural Computing & Applications 7 (4): 367–75. https://doi.org/10.1007/BF01428127.

Royston, P. 2001. “The Lognormal Distribution as a Model for Survival Time in Cancer, With an Emphasis on Prognostic Factors.” Statistica Neerlandica 55 (1): 89–104. https://doi.org/10.1111/1467-9574.00158.

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, Mahesh K B Parmar, and Douglas G Altman. 2008. “Visualizing Length of Survival in Time-to-Event Studies: A Complement to Kaplan–Meier Plots.” JNCI: Journal of the National Cancer Institute 100 (2): 92–97. https://doi.org/10.1093/jnci/djm265.

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 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.

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.

Saul, Alan D. 2016. “Gaussian Process Based Approaches for Survival Analysis.” University of Sheffield.

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.

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.

———. 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): 2588–2609. https://doi.org/10.1002/sim.5464.

Schwarzer, Guido, Werner Vach, and Martin Schumacher. 2010. “Estimation of prediction error for survival models.” Statistics in Medicine 29 (2): 262–74. 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.

Seker, H, M O Odetayo, D Petrovic, R N G Naguib, C Bartoli, L Alasio, M S Lakshmi, G V Sherbet, and O R Hinton. 2002. “An artificial neural network based feature evaluation index for the assessment of clinical factors in breast cancer survival analysis.” In IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373), 2:1211–1215 vol.2. https://doi.org/10.1109/CCECE.2002.1013121.

Seker, Huseyin, Michael O Odetayo, Dobrila Petrovic, Raouf N G Naguib, C Bartoli, L Alasio, M S Lakshmi, and G V Sherbet. 2002. “Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: statistical, neural network and fuzzy approaches.” Anticancer Research 22 (1A): 433–38. http://europepmc.org/abstract/MED/12017328.

Shiao, Han-Tai, and Vladimir Cherkassky. 2013. “SVM-based approaches for predictive modeling of survival data.” In Proceedings of the International Conference on Data Mining (DMIN), 1. The Steering Committee of The World Congress in Computer Science, Computer ….

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

Sonabend, Raphael. 2020. “survivalmodels: Models for Survival Analysis.” CRAN. https://raphaels1.r-universe.dev/ui#package:survivalmodels.

———. 2022. “Scoring rules in survival analysis,” December. http://arxiv.org/abs/2212.05260.

Sonabend, Raphael Edward Benjamin. 2021. “A Theoretical and Methodological Framework for Machine Learning in Survival Analysis: Enabling Transparent and Accessible Predictive Modelling on Right-Censored Time-to-Event Data.” PhD, University College London (UCL). https://discovery.ucl.ac.uk/id/eprint/10129352/.

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

Sonabend, Raphael, Franz J Király, Andreas Bender, Bernd Bischl, and Michel Lang. 2021. “mlr3proba: an R package for machine learning in survival analysis.” Edited by Jonathan Wren. Bioinformatics 37 (17): 2789–91. https://doi.org/10.1093/bioinformatics/btab039.

Sonabend, Raphael, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, and Sebastian Vollmer. 2022. “Flexible Group Fairness Metrics for Survival Analysis.” In DSHealth 2022 Workshop on Applied Data Science for Healthcare at KDD2022. http://arxiv.org/abs/2206.03256.

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.

Spooner, Annette, Emily Chen, Arcot Sowmya, Perminder Sachdev, Nicole A Kochan, Julian Trollor, and Henry Brodaty. 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.

Stasinopoulos, Mikis, Bob Rigby, Vlasios Voudouris, and Daniil Kiose. 2020. “gamlss.add: Extra Additive Terms for Generalized Additive Models for Location Scale and Shape.” CRAN. https://cran.r-project.org/package=gamlss.add.

Street, W Nick. 1998. “A Neural Network Model for Prognostic Prediction.” In Proceedings of the Fifteenth International Conference on Machine Learning. San Francisco.

Therneau, Terry M. 2015. “A Package for Survival Analysis in S.” https://cran.r-project.org/package=survival.

Therneau, Terry M., and Beth Atkinson. 2019. “rpart: Recursive Partitioning and Regression Trees.” CRAN.

Therneau, Terry M., and Elizabeth Atkinson. 2020. “Concordance.” https://cran.r-project.org/web/packages/survival/vignettes/concordance.pdf.

Therneau, Terry M., Patricia M. Grambsch, and Thomas R. Fleming. 1990. “Martingale-based residuals for survival models.” Biometrika 77 (1): 147–60. https://doi.org/10.1093/biomet/77.1.147.

Tsoumakas, Grigorios, and Ioannis Katakis. 2007. “Multi-Label Classification: An Overview.” International Journal of Data Warehousing and Mining 3 (3): 1–13. https://doi.org/10.4018/jdwm.2007070101.

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. Cham: 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.

Ushey, Kevin, J J Allaire, and Yuan Tang. 2020. “reticulate: Interface to ’Python’.” CRAN. https://cran.r-project.org/package=reticulate.

Van Belle, Vanya, Kristiaan Pelckmans, Johan A K Suykens, and Sabine Van Huffel. 2008. “Survival SVM: a practical scalable algorithm.” In 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 In Proceedings of the Third International Conference on Computational Intelligence in Medicine and Healthcare. 1.

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 Belle, Vanya, K Pelckmans, Johan A. K. Suykens, and Sabine Van Huffel. 2011. “Learning Transformation Models for Ranking and Survival Analysis.” Journal of Machine Learning Research 12: 819–62.

Van Belle, V, K Pelckmans, J A K Suykens, and S Van Huffel. 2010. “Additive survival least-squares support vector machines.” Statistics in Medicine 29 (2): 296–308. https://doi.org/10.1002/sim.3743.

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.

———. 2007. “Dynamic prediction by landmarking in event history analysis.” Scandinavian Journal of Statistics 34 (1): 70–85. https://doi.org/10.1111/j.1467-9469.2006.00529.x.

Vehtari, Aki, and Heikki Joensuu. 2013. “A Gaussian processes model for survival analysis with time dependent covariates and interval censoring.” https://users.aalto.fi/$\sim$ave/VehtariJoensuu_GIST_CT_timing_poster_2013.pdf.

Vinzamuri, Bhanukiran, Yan Li, and Chandan K. Reddy. 2017. “Pre-processing censored survival data using inverse covariance matrix based calibration.” IEEE Transactions on Knowledge and Data Engineering 29 (10): 2111–24. https://doi.org/10.1109/TKDE.2017.2719028.

Vock, David M, Julian Wolfson, Sunayan Bandyopadhyay, Gediminas Adomavicius, Paul E Johnson, Gabriela Vazquez-Benitez, and Patrick J O’Connor. 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/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, Hong, and Gang Li. 2017. “A Selective Review on Random Survival Forests for High Dimensional Data.” Quantitative Bio-Science 36 (2): 85–96. https://doi.org/10.22283/qbs.2017.36.2.85.

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, Zhu. 2019. “bujar: Buckley-James Regression for Survival Data with High-Dimensional Covariates.” CRAN. https://cran.r-project.org/package=bujar.

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 9 (1). https://doi.org/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.

Welchowski, Thomas, and Matthias Schmid. 2019. “discSurv: Discrete Time Survival Analysis.” CRAN. https://cran.r-project.org/package=discSurv.

Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

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.

Xiang, Anny, Pablo Lapuerta, Alex Ryutov, Jonathan Buckley, and Stanley Azen. 2000. “Comparison of the performance of neural network methods and Cox regression for censored survival data.” Computational Statistics & Data Analysis 34 (2): 243–57. https://doi.org/https://doi.org/10.1016/S0167-9473(99)00098-5.

Yang, Yanying. 2010. “Neural Network Survival Analysis.” PhD thesis, Universiteit Gent.

Yasodhara, Angeline, Mamatha Bhat, and Anna Goldenberg. 2018. Prediction of New Onset Diabetes after Liver Transplant.

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): 1095–1102. https://doi.org/10.1007/s00606-006-0435-8.

Zhang, Yucheng, Edrise M Lobo-Mueller, Paul Karanicolas, Steven Gallinger, Masoom A Haider, and Farzad Khalvati. 2020. “CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging.” BMC Medical Imaging 20 (1): 11. https://doi.org/10.1186/s12880-020-0418-1.

Zhao, Lili, and Dai Feng. 2020. “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.

Zhou, Zheng, Elham Rahme, Michal Abrahamowicz, and Louise Pilote. 2005. “Survival Bias Associated with Time-to-Treatment Initiation in Drug Effectiveness Evaluation: A Comparison of Methods.” American Journal of Epidemiology 162 (10): 1016–23. https://doi.org/10.1093/aje/kwi307.

Zhu, Wan, Longxiang Xie, Jianye Han, and Xiangqian Guo. 2020. “The Application of Deep Learning in Cancer Prognosis Prediction.” Cancers 12 (3): 603. https://doi.org/10.3390/cancers12030603.

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