Exploring Cumulative Effects in Survival Data Using Deep Learning Networks
This provides epidemiologists and researchers with a scalable and interpretable tool for analyzing cumulative effects in survival data, though it is incremental as it builds on existing neural network methods.
The paper tackled modeling cumulative effects of time-dependent exposures on survival outcomes by introducing CENNSurv, a deep learning approach that captured dynamic risk relationships, revealing multi-year lagged associations and short-term behavioral shifts in real-world datasets.
In epidemiological research, modeling the cumulative effects of time-dependent exposures on survival outcomes presents a challenge due to their intricate temporal dynamics. Conventional spline-based statistical methods, though effective, require repeated data transformation for each spline parameter tuning, with survival analysis computations relying on the entire dataset, posing difficulties for large datasets. Meanwhile, existing neural network-based survival analysis methods focus on accuracy but often overlook the interpretability of cumulative exposure patterns. To bridge this gap, we introduce CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data. Evaluated on two diverse real-world datasets, CENNSurv revealed a multi-year lagged association between chronic environmental exposure and a critical survival outcome, as well as a critical short-term behavioral shift prior to subscription lapse. This demonstrates CENNSurv's ability to model complex temporal patterns with improved scalability. CENNSurv provides researchers studying cumulative effects a practical tool with interpretable insights.