Statistical learning methods for improving predictive performance in time-dependent survival models.

ABSTRACT:

Background

The COVID-19 pandemic has highlighted the need for survival models to assess risk factors and time-dependent effects in infectious diseases. However, the Cox proportional hazards (PH) model, which assumes constant covariate effects, struggles to capture disease dynamics. This underscores the need for advanced models that incorporate time-dependent coefficients and covariates for improved accuracy.

Methods

To address the need for modeling time-dependent effects and covariates, we applied a stratified Cox PH model with multiple time intervals to better satisfy the PH assumption. We conducted simulations to evaluate the performance of machine learning and deep learning survival models, including random survival forest (RSF), DeepSurv, and DeepHit. To improve time-dep

SUBMITTER: Seo H 

PROVIDER: S-EPMC12400734 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature