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

arXiv: https://arxiv.org/abs/1606.03033 · HTML: https://mlsanity.com/paper/1606.03033/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [User Engagement in Mobile Health Applications](https://mlsanity.com/paper/2206.08178/) (2022)
- [The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems](https://mlsanity.com/paper/2409.16098/) (2024)
- [Adaptive Interventions for Global Health: A Case Study of Malaria](https://mlsanity.com/paper/2303.02075/) (2023)
- [Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings](https://mlsanity.com/paper/2408.07629/) (2024)
- [Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx](https://mlsanity.com/paper/2408.08024/) (2024)

## Abstract (arXiv)

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.