{
  "id": "1907.00825",
  "version": "v1",
  "published": "2019-07-01T14:34:03.000Z",
  "updated": "2019-07-01T14:34:03.000Z",
  "title": "Time-to-Event Prediction with Neural Networks and Cox Regression",
  "authors": [
    "Håvard Kvamme",
    "Ørnulf Borgan",
    "Ida Scheel"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-07-01T14:34:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural networks",
      "time-to-event prediction",
      "cox regression",
      "cox proportional hazards model",
      "loss function"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}