Tree models represent a novel and innovative way to analyze large datasets by partitioning the predictor space into simpler regions. The Bayesian ensemble tree regression model, which we introduce and explain in this article, uses the ensemble tree model in its structure, because combining multiple trees provides higher accuracy than a single tree. Therefore, this model is tree-based and a nonparametric model and is in fact a generalization of tree classification and regression methods, which have decision trees in their structure. These methods are powerful analysis for discovering data structure and their application in medical sciences is very wide.
In this method, priors are considered on the parameters of the ensemble tree model and then analyzed using auxiliary algorithms. In this article, we first briefly introduce the Bayesian ensemble tree regression model and then describe its application in survival analysis by examining data related to lung cancer patients.