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In this paper we utilize a survival analysis methodology incorporatingBayesian additive regression trees to account for nonlinear and additivecovariate effects. We compare the performance of Bayesian additive regressiontrees, Cox proportional hazards and random survival forests models for censoredsurvival data, using simulation studies and survival analysis for breast cancerwith U.S. SEER database for the year 2005. In simulation studies, we comparethe three models across varying sample sizes and censoring rates on the basisof bias and prediction accuracy. In survival analysis for breast cancer, weretrospectively analyze a subset of 1500 patients having invasive ductalcarcinoma that is a common form of breast cancer mostly affecting older woman.Predictive potential of the three models are then compared using some widelyused performance assessment measures in survival literature.

Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data