# Ensemble methods for survival function estimation with time-varying covariates

arXiv: https://arxiv.org/abs/2006.00567 · HTML: https://mlsanity.com/paper/2006.00567/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 4 of 4)

- [Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers](https://mlsanity.com/paper/2503.16645/) (2025)
- [SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms](https://mlsanity.com/paper/2412.07638/) (2024)
- [Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests](https://mlsanity.com/paper/2504.18498/) (2025)
- [Random Hazard Forests](https://mlsanity.com/paper/2608.21597/) (2026)

## Abstract (arXiv)

Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of survival function. However, the traditional survival forests - conditional inference forest, relative risk forest and random survival forest - have accommodated only time-invariant covariates. We generalize the conditional inference and relative risk forests to allow time-varying covariates. We also propose a general framework for estimation of a survival function in the presence of time-varying covariates. We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated L2 difference between the true and estimated survival functions. In general, the performance of the two proposed forests substantially improves over the Kaplan-Meier estimate. Taking into account all other factors, under the proportional hazard (PH) setting, the best method is always one of the two proposed forests, while under the non-PH setting, it is the adapted transformation forest. K-fold cross-validation is used as an effective tool to choose between the methods in practice.