Bagging survival trees.
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.
Hothorn
Torsten
T
Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University, Erlangen-Nuremberg, Waldstrasse 6, D-91054 Erlangen, Germany.
Lausen
Berthold
B
Benner
Axel
A
Radespiel-Tröger
Martin
M
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