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复发事件和失效时间数据的广义尺度变化模型的联合建模

Joint modeling of generalized scale-change models for recurrent event and failure time data.

作者信息

Wang Xiaoyu, Sun Liuquan

机构信息

Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.

出版信息

Lifetime Data Anal. 2023 Jan;29(1):1-33. doi: 10.1007/s10985-022-09573-5. Epub 2022 Sep 6.

Abstract

Recurrent event and failure time data arise frequently in many clinical and observational studies. In this article, we propose a joint modeling of generalized scale-change models for the recurrent event process and the failure time, and allow the two processes to be correlated through a shared frailty. The proposed joint model is flexible in that it requires neither the Poisson assumption for the recurrent event process nor a parametric assumption on the frailty distribution. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. Simulation studies are conducted to evaluate the finite sample performances of the proposed method. An application to a medical cost study of chronic heart failure patients is provided.

摘要

复发事件和失效时间数据在许多临床和观察性研究中经常出现。在本文中,我们提出了一种针对复发事件过程和失效时间的广义尺度变化模型的联合建模方法,并允许这两个过程通过共享脆弱性相互关联。所提出的联合模型具有灵活性,因为它既不需要对复发事件过程做泊松假设,也不需要对脆弱性分布做参数假设。我们开发了估计方程方法用于参数估计,并建立了所得估计量的渐近性质。进行了模拟研究以评估所提方法的有限样本性能。还提供了一个针对慢性心力衰竭患者医疗费用研究的应用实例。

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