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Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker
BACKGROUND: Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient’s biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520610/ https://www.ncbi.nlm.nih.gov/pubmed/34657597 http://dx.doi.org/10.1186/s12874-021-01375-x |