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Time-to-event prediction with neural networks and Cox regression. (English) Zbl 1440.62354
Summary: 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 https://github.com/havakv/pycox.
MSC:
62N02 Estimation in survival analysis and censored data
62M20 Inference from stochastic processes and prediction
62M45 Neural nets and related approaches to inference from stochastic processes
62J05 Linear regression; mixed models
60G55 Point processes (e.g., Poisson, Cox, Hawkes processes)
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