In long-term follow-up studies on recurrent events, the observation patterns may not be consistent over time. During some observation periods, subjects may be monitored continuously so that each event occurence time is known. While during the other observation periods, subjects may be monitored discretely so that only the number of events in each period is known. This results in mixed recurrent-event and panel-count data. In these data, there is dependence among within-subject events. Furthermore, if the data are collected from multiple centers, then there is another level of dependence among within-center subjects. Literature exists for clustered recurrent-event data, but not for clustered mixed recurrent-event and panel-count data. Ignoring the cluster effect may lead to less efficient analysis. In this paper, we present a marginal modeling approach to take into account the cluster effect and provide asymptotic distributions of the resulting regression parameters. Our simulation study demonstrates that this approach works well for practical situations. It was applied to a study comparing the hospitalization rates between childhood cancer survivors and healthy controls, with data collected from 26 medical institutions across North America during more than 20 years of follow-up.

中文翻译：

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应用于癌症幸存者研究的聚类混合复发事件数据的统计分析。

在对复发事件的长期随访研究中，随着时间的推移，观察模式可能不一致。在某些观察期间，可以连续监测受试者，以便知道每个事件发生的时间。而在其他观察期间，受试者可能会被离散监测，以便只知道每个时期的事件数量。这导致混合的复发事件和面板计数数据。在这些数据中，受试者内事件之间存在依赖性。此外，如果数据是从多个中心收集的，那么中心内受试者之间存在另一个水平的依赖性。存在关于聚集复发事件数据的文献，但没有关于聚集混合复发事件和面板计数数据的文献。忽略聚类效应可能会导致分析效率降低。在本文中，我们提出了一种边际建模方法来考虑集群效应并提供所得回归参数的渐近分布。我们的模拟研究表明，这种方法适用于实际情况。它被应用于一项比较儿童癌症幸存者和健康对照者的住院率的研究，数据来自北美 26 家医疗机构，并在 20 多年的随访期间收集了数据。