11/18/2016

Speaker: Yifei Sun, Department of Biostatistics, JHU

[Abstract]

The goal of this talk is to build up a unified framework for tree-structured analysis with binary response and survival data. Based on arguments from the Neyman-Pearson Lemma, we propose rank-based methods for growing and pruning classification and survival trees using concordance index. We define the concordance function as a map from an arbitrary function of the covariates to a real number, where the target function maximizes the concordance. In contrast with the existing methods where each split maximizes between node heterogeneity or within node homogeneity, our approach aims to maximize the concordance index of the current tree. For right-censored survival data, our framework has the flexibility to incorporate time-dependent covariates, resulting in more accurate prognostic models than only considering baseline covariates. The methods are applied to a transplant study for illustration.