Recurrent events are commonly encountered in longitudinal studies. The observation of recurrent events is often stopped by a dependent terminal event in practice. For this data scenario, we propose two sensible adaptations of the generalized accelerated recurrence time (GART) model (J. Amer. Statist. Assoc. 111 (2016) 145–156) to provide useful alternative analyses that can offer physical interpretations while rendering extra flexibility beyond the existing work based on the accelerated failure time model. Our modeling strategies align with the rationale underlying the use of the survivors’ rate function or the adjusted rate function to account for the presence of the dependent terminal event. For the proposed models, we identify and develop estimation and inference procedures which can be readily implemented based on existing software. We establish the asymptotic properties of the new estimator. Simulation studies demonstrate good finite-sample performance of the proposed methods. An application to a dataset from the Cystic Fibrosis Foundation Patient Registry (CFFPR) illustrates the practical utility of the new methods.

中文翻译：

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存在相关终端事件时的广义加速复发时间模型


纵向研究中经常遇到重复事件。在实践中，对重复事件的观察经常被依赖的终止事件停止。对于这种数据场景，我们提出了对广义加速复发时间 (GART) 模型的两种合理调整（ J. Amer. Statist. Assoc. 111 (2016) 145–156），以提供有用的替代分析，这些分析可以提供物理解释，同时渲染额外的数据。灵活性超出了基于加速故障时间模型的现有工作。我们的建模策略与使用幸存者率函数或调整后的率函数来解释相关终端事件的存在的基本原理一致。对于所提出的模型，我们确定并开发了可以根据现有软件轻松实施的估计和推理程序。我们建立新估计量的渐近性质。仿真研究表明所提出的方法具有良好的有限样本性能。对囊性纤维化基金会患者登记处 (CFFPR) 数据集的应用说明了新方法的实际用途。