Neural network-based survival methods can model data-driven covariate interactions. While these methods can 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 may take time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the 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 modeling framework for data-driven, time-varying interaction modeling of single event survival outcomes. An R package is available at https://github.com/Jesse-Islam/cbnn.

翻译：基于神经网络的生存分析方法能够建模数据驱动的协变量交互作用。尽管这些方法的预测性能优于基于回归的方法，但并非所有方法都能建模时变交互作用和复杂的基准风险函数。为解决这一问题，我们提出基于病例库的神经网络作为新方法，将病例库抽样框架与灵活的神经网络架构相结合。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，我们构建了一个可将时间作为输入的前馈神经网络。CBNN通过预测特定时刻事件发生的概率来估计风险函数。在仿真实验和三项案例研究中，我们采用两种时间依赖指标比较了CBNN与回归及基于神经网络的生存分析方法的性能。首先，在涉及复杂基准风险函数和时变交互作用的仿真中评估所有方法，CBNN的表现优于竞争对手。随后在三个真实数据应用中，CBNN在两个研究中优于竞争模型，在第三个研究中表现相当。我们的研究结果凸显了将病例库抽样与深度学习相结合的优势，为单事件生存结局的数据驱动型时变交互作用建模提供了简洁灵活的框架。R语言软件包托管于https://github.com/Jesse-Islam/cbnn。