pycox swMATH ID: 33183 Software Authors: Kvamme, Håvard; Borgan, Ørnulf; Scheel, Ida Description: Time-to-event prediction with neural networks and Cox regression. New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks. Building on methodology from nested case-control studies, we propose a loss function that scales well to large data sets and enables fitting of both proportional and non-proportional extensions of the Cox model. Through simulation studies, the proposed loss function is verified to be a good approximation for the Cox partial log-likelihood. The proposed methodology is compared to existing methodologies on real-world data sets and is found to be highly competitive, typically yielding the best performance in terms of Brier score and binomial log-likelihood. A Python package for the proposed methods is available at url{https://github.com/havakv/pycox}. Homepage: https://github.com/havakv/pycox Source Code: https://github.com/havakv/pycox Dependencies: PyTorch Keywords: Cox regression; customer churn; neural networks; non-proportional hazards; survival prediction Related Software: DeepSurv; survival; GitHub; dynpred; scikit-survival; Statsmodels; Python; Scikit; PyTorch Cited in: 4 Publications Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year Time-to-event prediction with neural networks and Cox regression. Zbl 1440.62354Kvamme, Håvard; Borgan, Ørnulf; Scheel, Ida 2019 all top 5 Cited by 7 Authors 2 Borgan, Ørnulf 2 Kvamme, Håvard 1 Cournède, Paul-Henry 1 Lemler, Sarah 1 Pölsterl, Sebastian 1 Sautreuil, Mathilde 1 Scheel, Ida Cited in 2 Serials 2 Journal of Machine Learning Research (JMLR) 1 Lifetime Data Analysis Cited in 4 Fields 3 Statistics (62-XX) 1 Probability theory and stochastic processes (60-XX) 1 Computer science (68-XX) 1 Biology and other natural sciences (92-XX) Citations by Year