论文标题
具有非比例危害的联合COX模型
A Federated Cox Model with Non-Proportional Hazards
论文作者
论文摘要
最近的研究表明，神经网络有可能改善经典生存模型，例如Cox模型，Cox模型广泛用于临床实践。但是，神经网络通常依赖于中心可用的数据，而医疗保健数据经常在安全孤岛中保存。我们提出了一个联合的COX模型，该模型可容纳此数据设置并放松比例危害假设，从而允许时间变化的协变量效应。在后一方面，我们的模型不需要明确的时间变化效果，而与以前的工作相比，降低了前期组织成本。我们尝试使用公开可用的临床数据集，并证明联合模型能够与标准模型一样执行。
Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice. Neural networks, however, typically rely on data that are centrally available, whereas healthcare data are frequently held in secure silos. We present a federated Cox model that accommodates this data setting and also relaxes the proportional hazards assumption, allowing time-varying covariate effects. In this latter respect, our model does not require explicit specification of the time-varying effects, reducing upfront organisational costs compared to previous works. We experiment with publicly available clinical datasets and demonstrate that the federated model is able to perform as well as a standard model.
