# An Ensemble Method for Interval-Censored Time-to-Event Data

arXiv: https://arxiv.org/abs/1901.04599 · HTML: https://mlsanity.com/paper/1901.04599/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Neural interval-censored survival regression with feature selection](https://mlsanity.com/paper/2206.06885/) (2022)
- [Boosting methods for interval-censored data with regression and classification](https://mlsanity.com/paper/2601.17973/) (2026)
- [ICODEN: Ordinary Differential Equation Neural Networks for Interval-Censored Data](https://mlsanity.com/paper/2602.10303/) (2026)

## Abstract (arXiv)

Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longitudinal studies generate interval-censored data; one common example occurs in medical studies that entail periodic follow-up. In this paper we propose a survival forest method for interval-censored data based on the conditional inference framework. We describe how this framework can be adapted to the situation of interval-censored data. We show that the tuning parameters have a non-negligible effect on the survival forest performance and guidance is provided on how to tune the parameters in a data-dependent way to improve the overall performance of the method. Using Monte Carlo simulations we find that the proposed survival forest is at least as effective as a survival tree method when the underlying model has a tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and outperforms the survival tree method and Cox model when the true relationship is nonlinear. We illustrate the application of the method on a tooth emergence data set.