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Survival Ensembles
Biostatistics (2006)
  • Torsten Hothorn
  • Peter Buhlmann
  • Sandrine Dudoit
  • Annette M. Molinaro
  • Mark J. van der Laan
We propose a unified and flexible framework for ensemble learning in the presence of censoring. For right-censored data, we introduce a random forest algorithm and a generic gradient boosting algorithm for the construction of prognostic and diagnostic models. The methodology is utilized for predicting the survival time of patients suffering from acute myeloid leukemia based on clinical and genetic covariates. Furthermore, we compare the diagnostic capabilities of the proposed censored data random forest and boosting methods, applied to the recurrence-free survival time of node-positive breast cancer patients, with previously published findings.
  • Censoring,
  • Cross-validation,
  • Ensemble methods,
  • IPC weights,
  • Loss function,
  • Prediction,
  • Prognostic factors,
  • Survival analysis
Publication Date
November, 2006
Citation Information
Torsten Hothorn, Peter Buhlmann, Sandrine Dudoit, Annette M. Molinaro, et al.. "Survival Ensembles" Biostatistics Vol. 7 Iss. 3 (2006)
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