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效应。尽管这类方法在预测性能上优于基于回归的方法，但并非所有方法都能处理时变交互效应和复杂的基线风险函数。为此，我们提出基于案例库的神经网络（CBNN），将案例库采样框架与灵活的神经网络架构相结合作为新方法。通过采用新颖的采样方案和数据增强技术来自然处理删失数据，我们构建了一个可将时间作为输入的前馈神经网络。CBNN通过预测给定时刻事件发生的概率来估计风险函数。我们利用两个时间依赖性指标，在模拟实验和三个案例研究中将CBNN的性能与基于回归及神经网络的生存分析方法进行对比。首先，在涉及复杂基线风险函数和时变交互效应的模拟中评估所有方法，CBNN的表现优于竞争模型。接着，将全部方法应用于三个真实数据场景，CBNN在两个研究中超越对比模型，在第三个研究中表现相近。研究结果凸显了将案例库采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时变交互建模提供了简洁灵活的建模框架。相关R软件包可通过https://github.com/Jesse-Islam/cbnn获取。