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Biostatistics Advance Access published online on December 12, 2005
Biostatistics, 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.
Received April 4, 2005
Revised November 4, 2005
Accepted December 7, 2005

Article

Survival Ensembles

Torsten Hothorn 1 *, Peter Bühlmann 2, Sandrine Dudoit 3, Annette Molinaro 4, and Mark J. van der Laan 3

1 Institut für Medizininformatik, Biometrie und Epidemiologie Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstraße 6, D-91054 Erlangen, Germany. Tel: ++49-9131-8522707 Fax: ++49-9131-8525740
2 Seminar für Statistik, ETH Zürich, CH-8032 Zürich, Switzerland
3 Division of Biostatistics, University of California, Berkeley 140 Earl Warren Hall, #7360, Berkeley, CA 94720-7360, USA
4 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

* To whom correspondence should be addressed.
Torsten Hothorn, E-mail: torsten.hothorn{at}rzmail.uni-erlangen.de


  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 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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