Improving the within-node estimation of survival trees while retaining interpretability

In statistical learning for survival data, survival trees are favored for their capacity to detect complex relationships beyond parametric and semiparametric models. Despite this, their prediction accuracy is often suboptimal. In this paper, we propose a new method based on super learning to improve the within-node estimation and overall survival prediction accuracy, while preserving the interpretability of the survival tree. Simulation studies reveal the proposed method's superior finite sample performance compared to conventional approaches for within-node estimation in survival trees. Furthermore, we apply this method to analyze the North Central Cancer Treatment Group Lung Cancer Data, cardiovascular medical records from the Faisalabad Institute of Cardiology, and the integrated genomic data of ovarian carcinoma with The Cancer Genome Atlas project.