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<p>We propose a novel maximum likelihood copula-based approach to analyze long-term survival data with dependent censoring. The promotion time cure rate model is assumed to accommodate the cure fraction within the survival time distribution, a joint distribution for the failure and censoring times is defined through the specification of Clayton and Plackett copulas, along with Weibull and piecewise exponential marginal distributions. A simulation study, taking into account different dependency scenarios, indicates that the proposed models perform well in terms of parameters estimation, with small bias and coverage probability close to the nominal value. Finally, we illustrate the usefulness of the proposed models with the analysis of a real dataset involving patients diagnosed with prostate cancer.</p>

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Cure rate regression models for dependent censoring under a copula-based approach

  • 期刊论文

  • 发表日期：

    2025年2月13日

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  • Maicon Gottselig,
  • Silvana Schneider,
  • Fábio Nogueira Demarqui

摘要

We propose a novel maximum likelihood copula-based approach to analyze long-term survival data with dependent censoring. The promotion time cure rate model is assumed to accommodate the cure fraction within the survival time distribution, a joint distribution for the failure and censoring times is defined through the specification of Clayton and Plackett copulas, along with Weibull and piecewise exponential marginal distributions. A simulation study, taking into account different dependency scenarios, indicates that the proposed models perform well in terms of parameters estimation, with small bias and coverage probability close to the nominal value. Finally, we illustrate the usefulness of the proposed models with the analysis of a real dataset involving patients diagnosed with prostate cancer.