Copula based dependent censoring in cure models

Morine Delhelle and Ingrid Van Keilegom
Additional contact information
Morine Delhelle: Université catholique de Louvain, LIDAM/ISBA, Belgium
Ingrid Van Keilegom: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2025025, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: In this paper we consider a time-to-event variable T that is subject to random right censoring, and we assume that the censoring time C is stochastically dependent on T and that there is a positive probability of not observing the event. There are various situations in practice in which this happens, and appropriate models and methods need to be considered to avoid biased estimators of the survival function or incorrect conclusions in clinical trials. In this work we propose a fully parametric mixture cure model for the bivariate distribution of (T, C), which deals with all these features. The model depends on a parametric copula and on parametric marginal distributions for T and C. A major advantage of our approach in comparison to existing approaches in the literature is that the copula which models the dependence between T and C is not assumed to be known, nor is the association parameter. Furthermore, our model allows for the identification and estimation of the cure fraction and the association between T and C, despite the fact that only the smallest of these variables is observable. Sufficient conditions are developed under which the model is identified, and an estimation procedure is proposed. The asymptotic behaviour of the estimated parameters is studied, and their finite sample performance is illustrated by means of a thorough simulation study and an analysis of breast cancer data.

Keywords: Copulas; Cure models; Dependent censoring; Identifiability; Inference; Survival analysis (search for similar items in EconPapers)
Pages: 22
Date: 2026-01-01
Note: In: Test, 2025, vol. 34(2), p. 361-382
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2025025

DOI: 10.1007/s11749-024-00961-7

Access Statistics for this paper

More papers in LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) Voie du Roman Pays 20, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Nadja Peiffer ().