title: Survival trees for left-truncated and right-censored data, with application to time-varying covariate data.
abstract: Tree methods (recursive partitioning) are a popular class of nonparametric methods for analyzing data. One extension of the basic tree methodology is the survival tree, which applies recursive partitioning to censored survival data. There are several existing survival tree methods in the literature, which are mainly designed for right-censored data. We propose two new survival trees for left-truncated and right-censored (LTRC) data, which can be seen as a generalization of the traditional survival tree for right-censored data. Further, we show that such trees can be used to analyze survival data with time-varying covariates, essentially building a time-varying covariates survival tree. Implementation of the methods is easy, and simulations and real data analysis results show that the proposed methods work well for LTRC data and survival data with time-varying covariates, respectively.
authors.affiliation: IOMS Department, Leonard N. Stern School of Business, NewYork University, 44W 4th Street, NewYork, NY 10012, USAwfu@stern.nyu.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Fu
authors.affiliation: IOMS Department, Leonard N. Stern School of Business, NewYork University, 44W 4th Street, NewYork, NY 10012, USAwfu@stern.nyu.edu.
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Simonoff
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D016019:Survival Analysis
keywords: Recursive partition algorithm; Survival analysis; Survival tree; Time-varying covariates survival data