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

TL;DR

This paper compares Bayesian additive regression trees, Cox models, and random survival forests for survival data, demonstrating their performance through simulations and breast cancer analysis, highlighting strengths and limitations.

Contribution

It introduces a comparative analysis of three advanced survival models, emphasizing Bayesian additive regression trees' effectiveness in handling nonlinear effects.

Findings

Bayesian additive regression trees show competitive prediction accuracy.

Model performance varies with sample size and censoring rate.

All models provide valuable insights, with specific strengths depending on data conditions.

Abstract

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.