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Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests
A crucial component of making individualized treatment decisions is to accurately predict each patient’s disease risk. In clinical oncology, disease risks are often measured through time-to-event data, such as overall survival and progression/recurrence-free survival, and are often subject to censor...
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| Publicado no: | J Biopharm Stat |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7196339/ https://ncbi.nlm.nih.gov/pubmed/29048993 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/10543406.2017.1377730 |
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