标题： 由带有分散参数的障碍零修改幂级数离散脆弱性诱导的贝叶斯生存模型：肺癌的应用 显示英文标题

标题： A Bayesian survival model induced by hurdle zero-modified power series discrete frailty with dispersion: an application in lung cancer

摘要： 脆弱性生存模型在临床和流行病学研究中被广泛用于捕捉个体间未观察到的异质性。 本文介绍了一种贝叶斯生存模型，该模型以障碍零修改幂级数（HZMPS）分布诱导的离散脆弱性为特征。 HZMPS 的一个关键特性是包含分散参数，增强了捕捉各种异质性模式的灵活性。 此外，这种脆弱性设定允许模型区分对感兴趣事件更易感的个体与可能已治愈或不再处于风险中的个体。 我们采用贝叶斯框架进行参数估计，即使在数据有限的情况下，也能实现先验信息的整合和稳健推断。 进行了模拟研究以探索模型的极限。 我们的提议还应用于肺癌研究中，患者间的变异性在疾病进展和治疗反应中起着至关重要的作用。 这项研究的结果强调了在生存数据分析中使用更灵活的脆弱性模型的重要性，并突出了贝叶斯方法在生物医学研究中建模异质性的潜力。 显示英文摘要

摘要： Frailty survival models are widely used to capture unobserved heterogeneity among individuals in clinical and epidemiological research. This paper introduces a Bayesian survival model that features discrete frailty induced by the hurdle zero-modified power series (HZMPS) distribution. A key characteristic of HZMPS is the inclusion of a dispersion parameter, enhancing flexibility in capturing diverse heterogeneity patterns. Furthermore, this frailty specification allows the model to distinguish individuals with higher susceptibility to the event of interest from those potentially cured or no longer at risk. We employ a Bayesian framework for parameter estimation, enabling the incorporation of prior information and robust inference, even with limited data. A simulation study is performed to explore the limits of the model. Our proposal is also applied to a lung cancer study, in which patient variability plays a crucial role in disease progression and treatment response. The findings of this study highlight the importance of more flexible frailty models in survival data analysis and emphasize the potential of the Bayesian approach to modeling heterogeneity in biomedical studies.