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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-024-02418-9 |
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