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In the context of survival analysis, data-driven neural network-based methodshave been developed to model complex covariate effects. While these methods mayprovide better predictive performance than regression-based approaches, not allcan model time-varying interactions and complex baseline hazards. To addressthis, we propose Case-Base Neural Networks (CBNNs) as a new approach thatcombines the case-base sampling framework with flexible neural networkarchitectures. Using a novel sampling scheme and data augmentation to naturallyaccount for censoring, we construct a feed-forward neural network that includestime as an input. CBNNs predict the probability of an event occurring at agiven moment to estimate the full hazard function. We compare the performanceof CBNNs to regression and neural network-based survival methods in asimulation and three case studies using two time-dependent metrics. First, weexamine performance on a simulation involving a complex baseline hazard andtime-varying interactions to assess all methods, with CBNN outperformingcompetitors. Then, we apply all methods to three real data applications, withCBNNs outperforming the competing models in two studies and showing similarperformance in the third. Our results highlight the benefit of combiningcase-base sampling with deep learning to provide a simple and flexibleframework for data-driven modeling of single event survival outcomes thatestimates time-varying effects and a complex baseline hazard by design. An Rpackage is available at https://github.com/Jesse-Islam/cbnn.

Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions