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

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.