Scoring rules are an established way of comparing predictive performances across model classes. In the context of survival analysis, they require adaptation in order to accommodate censoring. This work investigates using scoring rules for model training rather than evaluation. Doing so, we establish a general framework for training survival models that is model agnostic and can learn event time distributions parametrically or non-parametrically. In addition, our framework is not restricted to any specific scoring rule. While we focus on neural network-based implementations, we also provide proof-of-concept implementations using gradient boosting, generalized additive models, and trees. Empirical comparisons on synthetic and real-world data indicate that scoring rules can be successfully incorporated into model training and yield competitive predictive performance with established time-to-event models.

翻译：评分规则是一种比较不同模型类别预测性能的既定方法。在生存分析背景下，需要对其进行调整以适应删失数据。本研究探讨将评分规则用于模型训练而非评估。通过这种方式，我们建立了一个通用的生存模型训练框架，该框架与模型无关，能够以参数化或非参数化方式学习事件时间分布。此外，我们的框架不局限于任何特定的评分规则。虽然我们主要关注基于神经网络的实现，但也提供了使用梯度提升、广义可加模型和决策树的概念验证实现。在合成数据和真实数据上的实证比较表明，评分规则可以成功融入模型训练，并与现有的事件时间模型产生具有竞争力的预测性能。