We consider the semiparametric regression of panel count data occurring in longitudinal follow-up studies that concern occurrence rate of certain recurrent events. The analysis of panel count data involves two processes, i.e, a recurrent event process of interest and an observation process controlling observation times. However, the model assumptions of existing methods, such as independent censoring time and Poisson assumption, are restrictive and questionable. In this paper, we propose new joint models for panel count data by considering both informative observation times and censoring times. The asymptotic normality of the proposed estimators are established. Numerical results from simulation studies and a real data example show the advantage of the proposed method.

中文翻译：

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具有丰富信息的观察时间和审查时间的专家组计数数据的可靠估计。

我们考虑了涉及某些复发事件发生率的纵向随访研究中发生的小组计数数据的半参数回归。面板计数数据的分析涉及两个过程，即感兴趣的重复事件过程和控制观察时间的观察过程。但是，现有方法的模型假设（例如独立审查时间和泊松假设）是限制性且有问题的。在本文中，我们通过考虑信息量大的观察时间和审查时间，为面板计数数据提出了新的联合模型。提出了估计量的渐近正态性。仿真研究的数值结果和实际数据示例证明了该方法的优势。