Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data
This work addresses variable selection for survival data in medical research, but it is incremental as it compares existing methods without introducing new ones.
The paper compared Bayesian additive regression trees, Cox proportional hazards, and random survival forests for survival data, finding that Bayesian additive regression trees performed best in simulations and breast cancer analysis using the SEER database with 1500 patients.
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.