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

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...

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Bibliographic Details
Main Authors: Anya H. Fries, Eunji Choi, Summer S. Han
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Medical Research Methodology
Subjects:
Dynamic prediction
Landmark
Penalization
Prediction accuracy
Longitudinal data
Competing risks
Online Access:https://doi.org/10.1186/s12874-024-02418-9
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