title: Bagging survival trees.
abstract: Predicted survival probability functions of censored event free survival are improved by bagging survival trees. We suggest a new method to aggregate survival trees in order to obtain better predictions for breast cancer and lymphoma patients. A set of survival trees based on B bootstrap samples is computed. We define the aggregated Kaplan-Meier curve of a new observation by the Kaplan-Meier curve of all observations identified by the B leaves containing the new observation. The integrated Brier score is used for the evaluation of predictive models. We analyse data of a large trial on node positive breast cancer patients conducted by the German Breast Cancer Study Group and a smaller 'pilot' study on diffuse large B-cell lymphoma, where prognostic factors are derived from microarray expression values. In addition, simulation experiments underline the predictive power of our proposal.
authors.affiliation: Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University, Erlangen-Nuremberg, Waldstrasse 6, D-91054 Erlangen, Germany.
authors.forename: Torsten
authors.initials: T
authors.lastname: Hothorn
authors.forename: Berthold
authors.initials: B
authors.lastname: Lausen
authors.forename: Axel
authors.initials: A
authors.lastname: Benner
authors.forename: Martin
authors.initials: M
authors.lastname: Radespiel-Tröger
mesh_terms: D001943:Breast Neoplasms / Q000401:mortality; D002986:Clinical Trials as Topic; D005260:Female; D005858:Germany / Q000453:epidemiology; D006801:Humans; D008223:Lymphoma / Q000401:mortality; D008297:Male; D015233:Models, Statistical; D016019:Survival Analysis