论文标题
基于似然的仪器可变方法用于COX比例危险模型
Likelihood-based Instrumental Variable Methods for Cox Proportional Hazard Models
论文作者
论文摘要
在生物识别技术和相关领域中，COX比例危害模型被广泛用于通过协变量调整进行分析。但是，当未观察到某些协变量时，通常无法获得公正的估计量。即使有一些未测量的协变量，仪器变量方法也可以在某些假设下应用。在本文中，我们提出了COX比例危害模型的新仪器变量估计器。估算器与Martinez-Camblor等人，2019年相似，但完全不一样。我们使用有限信息的想法最大可能性。我们表明估计器具有良好的理论属性。另外，我们通过模拟数据集确认方法和以前的方法的属性。
In biometrics and related fields, the Cox proportional hazards model are widely used to analyze with covariate adjustment. However, when some covariates are not observed, an unbiased estimator usually cannot be obtained. Even if there are some unmeasured covariates, instrumental variable methods can be applied under some assumptions. In this paper, we propose the new instrumental variable estimator for the Cox proportional hazards model. The estimator is the similar feature as Martinez-Camblor et al., 2019, but not the same exactly; we use an idea of limited-information maximum likelihood. We show that the estimator has good theoretical properties. Also, we confirm properties of our method and previous methods through simulations datasets.
