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

TL;DR

This paper introduces two novel survival tree methods for left-truncated and right-censored data, extending traditional survival trees to handle more complex survival data, including time-varying covariates, with demonstrated effectiveness through simulations and real data analysis.

Contribution

The paper presents new survival tree algorithms for LTRC data and time-varying covariates, generalizing existing methods for broader applicability.

Findings

Effective in analyzing LTRC data

Works well with survival data with time-varying covariates

Easy to implement and computationally efficient

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.