References
Aalen, Odd. 1978. “Nonparametric Inference
for a Family of Counting Processes.” The Annals of
Statistics 6 (4): 701–26.
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.
Akritas, Michael G., and Michael P. LaValley. 2005. “A Generalized
Product-Limit Estimator for Truncated Data.” Nonparametric
Statistics, September. https://doi.org/10.1080/10485250500038637.
Andersen, Per Kragh, Mette Gerster Hansen, and John P. Klein. 2004.
“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.
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, 145–55. https://proceedings.mlr.press/v115/avati20a.html
http://arxiv.org/abs/1806.08324.
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.
Bender, Andreas, and Fabian Scheipl. 2018. “pammtools: Piece-wise exponential Additive Mixed Modeling
tools.” arXiv:1806.01042 [Stat]. http://arxiv.org/abs/1806.01042.
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. 2013. “CoxBoost: Cox models
by likelihood based boosting for a single survival endpoint or competing
risks.” CRAN.
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.
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.
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.
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.
———. 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.
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, 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: 210–18.
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 (1): 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.
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.
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.
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.
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.
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/.
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.
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.
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.
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.
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.
Hornung, Roman, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig
Bothmann, Bernd Bischl, Thomas Augustin, and Anne-Laure Boulesteix.
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, 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.
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. 1989. “Regression and time series model selection in small
samples.” Biometrika 76 (2): 297–307. 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.
Ishwaran, Hemant, Udaya B Kogalur, Xi Chen, and Andy J Minn. 2011.
“Random Survival Forests for High-Dimensional
Data.” Statistical Analysis and Data Mining 4
(1): 115–32. https://doi.org/10.1002/sam.
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. 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.
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.
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.
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.
Kuhn, Max, and Julia Silge. 2023. Tidy Modeling with
R. https://www.tmwr.org/.
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.
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):
5487–5500. https://doi.org/https://doi.org/10.1002/sim.9136.
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.
Nelson, Wayne. 1972. “Theory and Applications
of Hazard Plotting for Censored Failure Data.”
Technometrics 14 (4): 945–66.
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.
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.
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.
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.
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:
172–81.
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.
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.
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.
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.
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.
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, 173–86. Boston, MA: Springer US. https://doi.org/10.1007/978-0-387-47509-7_8.
Sonabend, Raphael. 2020. “survivalmodels:
Models for Survival Analysis.” CRAN. https://raphaels1.r-universe.dev/ui#package:survivalmodels.
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.
Sonabend, Raphael, John Zobolas, Philipp Kopper, Lukas Burk, and Andreas
Bender. 2024. “Examining properness in the
external validation of survival models with squared and logarithmic
losses,” December. http://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.
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.
Suresh, Krithika, Cameron Severn, and Debashis Ghosh. 2022. “Survival prediction models: an introduction to
discrete-time modeling.” BMC Medical Research
Methodology 22 (1): 207. https://doi.org/10.1186/s12874-022-01679-6.
Therneau, Terry M. 2015. “A Package for
Survival Analysis in S.” https://cran.r-project.org/package=survival.
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.
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.” 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 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.
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, 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, 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.