# Survival Trees for Interval-Censored Survival data

## TL;DR

This paper introduces a survival tree method tailored for interval-censored data, effectively capturing complex survival relationships and outperforming traditional models in non-linear scenarios.

## Contribution

The paper presents a novel survival tree approach for interval-censored data based on the conditional inference framework, demonstrating its effectiveness through simulations and real data.

## Key findings

- Performs similarly to Cox model in linear cases
- Outperforms Cox model in non-linear relationships
- Outperforms imputation-based survival trees

## Abstract

Interval-censored data, in which the event time is only known to lie in some time interval, arise commonly in practice; for example, in a medical study in which patients visit clinics or hospitals at pre-scheduled times, and the events of interest occur between visits. Such data are appropriately analyzed using methods that account for this uncertainty in event time measurement. In this paper we propose a survival tree method for interval-censored data based on the conditional inference framework. Using Monte Carlo simulations we find that the tree is effective in uncovering underlying tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and performs at least as well as (and in the presence of right-censoring outperforms) the Cox model when the true relationship is not linear. Further, the interval-censored tree outperforms survival trees based on imputing the event time as an endpoint or the midpoint of the censoring interval. We illustrate the application of the method on tooth emergence data.