  In this paper we utilize a survival analysis methodology incorporating
Bayesian additive regression trees to account for nonlinear and additive
covariate effects. We compare the performance of Bayesian additive regression
trees, Cox proportional hazards and random survival forests models for censored
survival data, using simulation studies and survival analysis for breast cancer
with U.S. SEER database for the year 2005. In simulation studies, we compare
the three models across varying sample sizes and censoring rates on the basis
of bias and prediction accuracy. In survival analysis for breast cancer, we
retrospectively analyze a subset of 1500 patients having invasive ductal
carcinoma that is a common form of breast cancer mostly affecting older woman.
Predictive potential of the three models are then compared using some widely
used performance assessment measures in survival literature.
