Title
Abstract
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 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.
Disciplines
Multivariate Analysis | Statistical Models | Survival Analysis
Suggested Citation
Hothorn, Torsten; Buhlmann, Peter; Dudoit, Sandrine; Molinaro, Annette M.; and van der Laan, Mark J., "Survival Ensembles" (April 2005). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 174.
https://biostats.bepress.com/ucbbiostat/paper174
Comments
Published 2006 in Biostatistics, 7(3), 355-373.