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.

53 pages, 17 figures

References in corpus (1)

• Survival trees for left-truncated and right-censored data, with application to time-varying covariate data

Cited by in corpus (6)

• Survival trees for left-truncated and right-censored data, with application to time-varying covariate data
• The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems
• Survival trees for right-censored data based on score based parameter instability test
• A framework for leveraging machine learning tools to estimate personalized survival curves
• ROC-Guided Survival Trees and Ensembles
• Survival Trees for Interval-Censored Survival data