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Biostatistics Advance Access originally published online on December 12, 2005
Biostatistics 2006 7(3):355-373; doi:10.1093/biostatistics/kxj011
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Survival ensembles

Torsten Hothorn*

Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstraße 6, D-91054 Erlangen, Germany, Torsten.Hothorn{at}rzmail.uni-erlangen.de

Peter Bühlmann

Seminar für Statistik, ETH Zürich, CH-8032 Zürich, Switzerland

Sandrine Dudoit

Division of Biostatistics, University of California, Berkeley, 140 Earl Warren Hall, #7360, Berkeley, CA 94720-7360, USA

Annette Molinaro

Division of Biostatistics, Epidemiology and Public Health, Yale University School of Medicine, 206 LEPH, 60 College Street, PO Box 208034, New Haven CT 06520-8034, USA

Mark J. Van Der Laan

Division of Biostatistics, University of California, Berkeley, 140 Earl Warren Hall, #7360, Berkeley, CA 94720-7360, USA

* To whom correspondence should be addressed.

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.

Keywords: Censoring; Cross-validation; Ensemble methods; IPC weights; Loss function; Prediction; Prognostic factors; Survival analysis


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