Yu Guanglei, Zhu Liang, Li Yang, Sun Jianguo, Robison Leslie L
Department of Statistics, University of Missouri, Columbia, MO, U.S.A.
Biostatistics and Epidemiology Research Design, University of Texas Health Science Center at Houston, Houston, TX, U.S.A.
Stat Med. 2017 May 10;36(10):1669-1680. doi: 10.1002/sim.7217. Epub 2017 Jan 18.
Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd.
事件史研究在许多领域普遍开展，并且已经有大量文献用于分析这些研究中常见的两类数据：复发事件数据和面板计数数据。如果对所有研究对象进行连续随访，就会产生前者；而后者意味着每个研究对象仅在离散时间点进行观察。在现实中，可能会出现第三种类型的数据，即前两类数据的混合，此外，与前两类数据一样，可能存在一个相关的终端事件，这可能会排除感兴趣的复发事件的发生。本文讨论了在存在终端事件的情况下对混合复发事件和面板计数数据的回归分析，并提出了一种基于估计方程的方法来估计感兴趣的回归参数。此外，建立了所提出估计量的渐近性质，并且进行了模拟研究以评估所提方法的有限样本性能，结果表明该方法在实际情况下效果良好。最后，将该方法应用于一项激发本研究的儿童癌症研究。版权所有© 2017约翰威立父子有限公司。
