Survival ensembles.
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.
Hothorn
Torsten
T
Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstrasse 6, D-91054 Erlangen, Germany. Torsten.Hothorn@rzmail.uni-erlangen.de
Bühlmann
Peter
P
Dudoit
Sandrine
S
Molinaro
Annette
A
van der Laan
Mark J
MJ
D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology; D005260:Female; D006801:Humans; D015479:Leukemia, Myelomonocytic, Acute / Q000175:diagnosis; D008207:Lymphatic Metastasis / Q000473:pathology; D015233:Models, Statistical; D011379:Prognosis; D016019:Survival Analysis