Recent advances in copula-based methods for dependent censoring
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Gilles Crommen
[1]
;
Negera Wakgari Deresa
[1]
;
Myrthe D'Haen
[1]
;
Jie Ding
[1]
;
Ilias Willems
[1]
;
Ingrid Van Keilegom
[1]
-
[1] KU Leuven
KU Leuven
Arrondissement Leuven, Bélgica
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- Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 49, Nº. 1, 2025, págs. 3-41
- Idioma: inglés
- DOI: 10.57645/20.8080.02.21
- Enlaces
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Resumen
When modeling time-to-event data that are subject to right censoring, it is commonly as- sumed that the survival time T and the censoring time C are independent. However, this assumption frequently fails in practice, leading to biased estimators and testing proce- dures having invalid type 1 error rates. To overcome this issue, several models relaxing the independent censoring assumption have been proposed in the literature. Among these, copula-based approaches have become popular due to their ability to separately model the marginal distributions of T and C and their dependence structure. This review paper gives a comprehensive overview of recent advances in copula-based methods for dependent censoring, along with a discussion of the most important historical papers on this topic. As it is well known that the distribution of (T,C) (and hence of T ) is not identifed in a fully nonparametric way, we examine different strategies to achieve model identifability. These strategies consist of imposing assumptions on either the copula or the marginal distributions of T and C. Both of these approaches will be discussed, with and without covariates. We also consider the case where a dependent censoring time is accompanied by an additional latent independent censoring time. Lastly, we briefy explain alternative approaches that are not based on copulas.
