In survival analysis, dependent censoring poses significant challenges in accurately estimating model parameters and survival functions. This study introduces a novel framework leveraging Extended Generalized Marshall-Olkin (EGMO) models to address dependent censoring mechanisms. Geometric optimization techniques are employed to develop efficient estimation procedures that capture dependencies between failure and censoring times. We establish their asymptotic properties. Simulation studies and real data applications illustrate the method's robustness and effectiveness.

翻译：在生存分析中，相依删失对模型参数和生存函数的准确估计构成了重大挑战。本研究引入了一种基于扩展广义Marshall-Olkin（EGMO）模型的新框架，以处理相依删失机制。我们采用几何优化技术，开发出能够捕获失效时间与删失时间之间依赖关系的高效估计方法，并建立了其渐近性质。模拟研究与实际数据应用验证了该方法的稳健性和有效性。