The super learner for time-to-event outcomes: A tutorial.
Ruth Keogh, Karla Diaz-Ordaz, Nan van Geloven, Jon Michael Gran, Kamaryn T. Tanner.
https://arxiv.org/abs/2509.03315
Code illustrating the use of the superlearner for time-to-event outcomes. Three methods are used: the discrete-time super learner of Polley and van der Laan (2011), the continuous-time super learners of Westling et al. (2023) and Munch and Gerds (2024,2025). The methods are applied to the rotterdam data set available in the 'survival' package in R. Methods are implemented using the packages 'superlearner' and 'survSuperLearner' and code from the 'joint survival super learner' (jossl) repository. By-hand implementations are also provided to clearly illustrate the steps involved.
References
Munch, A. and Gerds, T.A. (2024). The state learner – a super learner for right-censored data. arXiv, arXiv:2405.17259.
Munch, A. and Gerds, T.A. (2025). The joint survival super learner: A super learner for right-censored data. arXiv, arXiv:2405.17259v2
Munch, A. and Gerds, T.A. (2025). The joint survival super learner (jossl). GitHub repository. https://github.com/amnudn/joint-survival-super-learner
Polley, E.C. and van der Laan, M.J. (2011). Super learning for right-censored data, 1st edition. New York: Springer, pp. 249–258.
Polley, E., LeDell, E., Chris Kennedy, C., Sam Lendle, S. and van der Laan, M. (2024). Super- Learner: Super Learner Prediction. R package version 2.0-29. https://CRAN.R-project.org/package=SuperLearner
Westling, T., Luedtke, A., Gilbert, P. B. and Carone, M. (2023). Inference for treatment-specific survival curves using machine learning. Journal of the American Statistical Association 119(546), 1541–1553.
Westling, T. and Elder, A. survSuperLearner: Super learning of conditional survival functions with right-censored time-to-event outcomes in discrete or continuous time. GitHub repository. https://github.com/tedwestling/survSuperLearner