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

TL;DR

This paper introduces Clayton-boost, a novel boosting method for time-to-event prediction that effectively models dependent censoring using a Clayton copula, reducing bias and outperforming traditional methods under dependency.

Contribution

The paper presents Clayton-boost, a new copula-based boosting approach that handles dependent censoring in survival analysis, removing the need for the independent censoring assumption.

Findings

Clayton-boost reduces prediction bias under dependent censoring.

It outperforms existing methods when dependency or censoring percentage is high.

The approach demonstrates the importance of modeling dependency in real-world data.

Abstract

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.