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Dependent censoring based on parametric copulas

Author

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  • C Czado
  • I Van Keilegom

Abstract

SummaryConsider a survival timethat is subject to random right censoring, and suppose thatis stochastically dependent on the censoring time . We are interested in the marginal distribution of . This situation is often encountered in practice. Consider, for example, the case whereis a patient’s time to death from a certain disease. Then the censoring timecould be the time until the patient leaves the study or the time until death from another cause. If the reason for leaving the study is related to the health condition of the patient, or if the patient dies from a disease that has similar risk factors to the disease of interest, thenandare likely to be dependent. In this paper we propose a new model that takes such dependence into account. The model is based on a parametric copula for the relationship betweenand , and on parametric marginal distributions forand . Unlike most other authors, we do not assume that the parameter defining the copula is known. We give sufficient conditions on these parametric copulas and marginals under which the bivariate distribution ofis identified. These sufficient conditions are then checked for a wide range of common copulas and marginals. We also study the estimation of the model, and carry out extensive simulations and analysis on a pancreatic cancer dataset to illustrate the proposed model and estimation procedure.

Suggested Citation

  • C Czado & I Van Keilegom, 2023. "Dependent censoring based on parametric copulas," Biometrika, Biometrika Trust, vol. 110(3), pages 721-738.
    • Handle: RePEc:oup:biomet:v:110:y:2023:i:3:p:721-738.
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    File URL: http://hdl.handle.net/10.1093/biomet/asac067
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    Citations

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    Cited by:

    1. Negera Wakgari Deresa & Ingrid Van Keilegom, 2025. "Semiparametric transformation models for survival data with dependent censoring," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(3), pages 425-457, June.
    2. Gilles Crommen & Jad Beyhum & Ingrid Van Keilegom, 2024. "An instrumental variable approach under dependent censoring," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(2), pages 473-495, June.
    3. Florian Stijven & Geert Molenberghs & Ingrid Keilegom & Wim Elst & Ariel Alonso, 2025. "Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(1), pages 1-23, January.
    4. Gilles Crommen & Jad Beyhum & Ingrid Van Keilegom, 2025. "Estimation of the complier causal hazard ratio under dependent censoring," Papers 2504.02096, arXiv.org.
    5. Escobar-Bach, Mikael & Helali, Salima, 2024. "Dependent censoring with simultaneous death times based on the Generalized Marshall–Olkin model," Journal of Multivariate Analysis, Elsevier, vol. 204(C).

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