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

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