In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that includes time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the full hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible framework for data-driven modeling of single event survival outcomes that estimates time-varying effects and a complex baseline hazard by design. An R package is available at https://github.com/Jesse-Islam/cbnn.

翻译：在生存分析领域，基于数据驱动的神经网络方法已被开发用于建模复杂的协变量效应。尽管这些方法可能比基于回归的方法提供更好的预测性能，但并非所有方法都能建模时变交互和复杂基线风险函数。为此，我们提出病例-基准神经网络（CBNNs）作为一种新方法，将病例-基准抽样框架与灵活的网络架构相结合。通过采用新颖的抽样方案和数据增强技术自然处理删失数据，我们构建了一个包含时间作为输入的馈入神经网络。CBNNs通过预测特定时刻事件发生的概率来估计完整风险函数。我们使用两个时间依赖指标，通过仿真研究与三项案例研究对比了CBNNs与回归和基于神经网络的生存方法的性能。首先，在涉及复杂基线风险函数和时变交互的仿真实验中评估了所有方法，结果表明CBNNs优于其他方法。随后，我们将所有方法应用于三个真实数据场景，CBNNs在两个研究中超越竞争模型，在第三个研究中表现出相似性能。我们的结果凸显了将病例-基准抽样与深度学习相结合的优势，可提供一个简单灵活的框架，用于对单事件生存结局进行数据驱动建模，并通过设计自动估计时变效应和复杂基线风险函数。相关R包可通过https://github.com/Jesse-Islam/cbnn获取。