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Survival Ensembles

• Torsten Hothorn, Institut fur Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Germany
• Peter Buhlmann, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
• Sandrine Dudoit, Division of Biostatistics, School of Public Health, University of California, Berkeley
• Annette M. Molinaro, Division of Cancer Epidemiology & Genetics, National Cancer Institute, NIH, Bethesda, MD
• Mark J. van der Laan, Division of Biostatistics, School of Public Health, University of California, Berkeley

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Article comments:

Published 2006 in Biostatistics, 7(3), 355-373.

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.

Subject Area:

Multivariate Analysis, Statistical Models, Survival Analysis

Suggested Citation:

Torsten Hothorn, Peter Buhlmann, Sandrine Dudoit, Annette M. Molinaro, and Mark J. van der Laan, "Survival Ensembles" (April 2005). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 174.
http://www.bepress.com/ucbbiostat/paper174