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Semiparametric transformation models for survival data with dependent censoring

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Listed:
  • Negera Wakgari Deresa

    (ORSTAT, KU Leuven)

  • Ingrid Van Keilegom

    (ORSTAT, KU Leuven)

Abstract

This paper proposes copula based semiparametric transformation models to take dependent censoring into account. The model is based on a parametric Archimedean copula model for the relation between the survival time ( $$T_1$$ T 1 ) and the censoring time ( $$T_2$$ T 2 ), whereas the marginal distributions of $$T_1$$ T 1 and $$T_2$$ T 2 follow a semiparametric transformation model. We show that this flexible model is identified based on the distribution of the observable variables, and propose estimators of the nonparametric functions and the finite dimensional parameters. An estimation algorithm is provided for implementing the new method. We establish the asymptotic properties of the estimators of the model parameters and the nonparametric functions. The theoretical development can serve as a valuable template when dealing with estimating equations that involve systems of linear differential equations. We also investigate the performance of the proposed method using finite sample simulations and real data example.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:aistmt:v:77:y:2025:i:3:d:10.1007_s10463-024-00921-w
    DOI: 10.1007/s10463-024-00921-w
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    References listed on IDEAS

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    1. Yi‐Hau Chen, 2010. "Semiparametric marginal regression analysis for dependent competing risks under an assumed copula," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 235-251, March.
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    6. Negera Wakgari Deresa & Ingrid Van Keilegom, 2024. "Copula Based Cox Proportional Hazards Models for Dependent Censoring," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(546), pages 1044-1054, April.
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