Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions.

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.

Clarifying the causal contrast: An empirical example applying the prevalent new user study design.

Purpose: The prevalent new user design extends the active comparator new user design to include patients switching to a treatment of interest from a comparator. We examined the impact of adding "switchers" to incident new users on the estimated hazard ratio (HR) of hospitalized heart failure.

Methods: Using MarketScan claims data (2000-2014), we estimated HRs of hospitalized heart failure between patients initiating GLP-1 receptor agonists (GLP-1 RA) and sulfonylureas (SU).