Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data.

ABSTRACT:

Background

To effectively monitor long-term outcomes among cancer patients, it is critical to accurately assess patients' dynamic prognosis, which often involves utilizing multiple data sources (e.g., tumor registries, treatment histories, and patient-reported outcomes). However, challenges arise in selecting features to predict patient outcomes from high-dimensional data, aligning longitudinal measurements from multiple sources, and evaluating dynamic model performance.

Methods

We provide a framework for dynamic risk prediction using the penalized landmark supermodel (penLM) and develop novel metrics ([Formula: see text] and [Formula: see text]) to evaluate and summarize model performance across different timepoints. Through simulations, we assess the coverage of the propos

SUBMITTER: Fries AH 

PROVIDER: S-EPMC11771018 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature