# A copula-based boosting model for time-to-event prediction with dependent censoring

arXiv: https://arxiv.org/abs/2210.04869 · HTML: https://mlsanity.com/paper/2210.04869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 98 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling](https://mlsanity.com/paper/1403.1452/) (2014)
- [The Brier Score under Administrative Censoring: Problems and Solutions](https://mlsanity.com/paper/1912.08581/) (2019)
- [A Decision Support System for Safer Airplane Landings: Predicting Runway Conditions Using XGBoost and Explainable AI](https://mlsanity.com/paper/2107.04010/) (2021)
- [Effect of right censoring bias on survival analysis](https://mlsanity.com/paper/2012.08649/) (2020)
- [Extending Statistical Boosting - An Overview of Recent Methodological Developments](https://mlsanity.com/paper/1403.1692/) (2014)
- [Dependent censoring based on copulas](https://mlsanity.com/paper/2104.06872/) (2021)

## Built on by (most cited first, 1 of 1)

- [Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees](https://mlsanity.com/paper/2312.15566/) (2023)

## Abstract (arXiv)

A characteristic feature of time-to-event data analysis is possible censoring of the event time. Most of the statistical learning methods for handling censored data are limited by the assumption of independent censoring, even if this can lead to biased predictions when the assumption does not hold. This paper introduces Clayton-boost, a boosting approach built upon the accelerated failure time model, which uses a Clayton copula to handle the dependency between the event and censoring distributions. By taking advantage of a copula, the independent censoring assumption is not needed any more. During comparisons with commonly used methods, Clayton-boost shows a strong ability to remove prediction bias at the presence of dependent censoring and outperforms the comparing methods either if the dependency strength or percentage censoring are considerable. The encouraging performance of Clayton-boost shows that there is indeed reasons to be critical about the independent censoring assumption, and that real-world data could highly benefit from modelling the potential dependency.