Exploring Cumulative Effects in Survival Data Using Deep Learning Networks

TL;DR

CENNSurv is a deep learning model designed to analyze complex, time-dependent survival data, offering improved scalability and interpretability over traditional methods, and revealing meaningful temporal exposure-outcome relationships.

Contribution

We introduce CENNSurv, a novel deep learning framework that effectively models dynamic risk in survival data with interpretability and scalability advantages.

Findings

Identified multi-year lagged effects of environmental exposure on survival.

Detected short-term behavioral shifts prior to subscription lapses.

Demonstrated improved scalability over traditional spline-based methods.

Abstract

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.