Decision tree for modeling survival data with competing risks

Abstract This work considers decision tree for modeling survival data with competing risks. A Survival Classification and Regression Tree (SCART) technique is proposed for analysing survival data by modifying classification and regression tree (CART) algorithm to handle censored data for both regression and classification problems. Different performance measures for regression and classification tree are proposed. Model validation is done by two different cross-validation methods. Two real life data sets are analyzed for illustration. It is found that the proposed method improve upon the existing classical method for analysis of survival data with competing risks.

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