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...
| 出版年: | BMC Medical Research Methodology |
|---|---|
| 主要な著者: | , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
BMC
2025-01-01
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| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1186/s12874-024-02418-9 |