A comparative study of methods for dynamic survival analysis.

ABSTRACT:

Introduction

Dynamic survival analysis has become an effective approach for predicting time-to-event outcomes based on longitudinal data in neurology, cognitive health, and other health-related domains. With advancements in machine learning, several new methods have been introduced, often using a two-stage approach: first extracting features from longitudinal trajectories and then using these to predict survival probabilities.

Methods

This work compares several combinations of longitudinal and survival models, assessing their predictive performance across different training strategies. Using synthetic and real-world cognitive health data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we explore the strengths and limitations of each model.

Results

Among t

SUBMITTER: de Swart WK 

PROVIDER: S-EPMC11876041 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature