Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data
The proposed penLM framework with novel evaluation metrics offers effective dynamic risk prediction when leveraging high-dimensional multi-source longitudinal data.
Authors
- Eunji Choi (ORCID: https://orcid.org/0000-0003-1315-1433)
- Summer S. Han (ORCID: https://orcid.org/0000-0002-2013-6883)
- A. Fries
Institutions
- Cornell University (US)
- Université Bourgogne Franche-Comté (FR)
- Stanford Medicine (US)
- Weill Cornell Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1186/s12874-024-02418-9
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- National Institutes of Health
- National Cancer Institute