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Activity Number: 250
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #319057 View Presentation
Title: Survival Trees for Left-Truncated and Right-Censored Data, with Application to Time-Varying Covariate Data
Author(s): Wei Fu* and Jeffrey Simonoff
Companies: and New York University
Keywords: Survival analysis ; Survival tree ; left-truncation and right censored data ; time-varying covariates

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 a new survival tree 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 a tree can be used to analyze survival data with time-varying covariates, essentially building a time-varying covariates survival tree. Implementation of the method is easy, and simulations and real data analysis results show that the proposed methods work well for both LTRC data and survival data with time-varying covariates.

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