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Defective models induced by gamma frailty term for survival data with cured fraction

机译:γ脆弱的术语诱导模型,用于固化分数的生存数据

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In this paper, we propose a defective model induced by a frailty term for modeling the proportion of cured. Unlike most of the cure rate models, defective models have advantage of modeling the cure rate without adding any extra parameter in model. The introduction of an unobserved heterogeneity among individuals has bring advantages for the estimated model. The influence of unobserved covariates is incorporated using a proportional hazard model. The frailty term assumed to follow a gamma distribution is introduced on the hazard rate to control the unobservable heterogeneity of the patients. We assume that the baseline distribution follows a Gompertz and inverse Gaussian defective distributions. Thus we propose and discuss two defective distributions: the defective gamma-Gompertz and gamma-inverse Gaussian regression models. Simulation studies are performed to verify the asymptotic properties of the maximum likelihood estimator. Lastly, in order to illustrate the proposed model, we present three applications in real data sets, in which one of them we are using for the first time, related to a study about breast cancer in the A.C. Camargo Cancer Center, Sao Paulo, Brazil.
机译:在本文中,我们提出了一种缺陷的模型,用于模拟固化比例的脆弱术语。与大多数固化速率模型不同,有缺陷的模型有利于建模治愈率而不在模型中添加任何额外参数。在个人之间引入未观察到的异质性,对估计模型带来了优势。使用比例危险模型纳入未观察的协变量的影响。假定遵循伽玛分布的脆弱术语是在危险率上引入控制患者的不可观察的异质性。我们假设基线分布遵循Gompertz和逆高斯有缺陷的分布。因此,我们提出并讨论了两个有缺陷的分布:啮合的伽马 - Gompertz和伽马 - 逆高斯回归模型。进行仿真研究以验证最大似然估计器的渐近特性。最后,为了说明所提出的模型,我们在真实数据集中展示了三个应用程序,其中我们第一次使用了其中一个,与AC Camargo癌症中心,圣保罗,巴西的乳腺癌研究有关。 。

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