Yu Guanglei, Zhu Liang, Sun Jianguo, Robison Leslie L
Department of Statistics, University of Missouri-Columbia, Columbia, MO, USA, 65211.
Biostatistics and Epidemiology Research Design, University of Texas Health Science Center at Houston, Houston, TX, USA, 77030.
Stat Interface. 2018;11(1):91-97. doi: 10.4310/SII.2018.v11.n1.a8.
This paper discusses regression analysis of a type of incomplete mixed data arising from event history studies with the proportional rates model. By mixed data, we mean that each study subject may be observed continuously during the whole study period, continuously over some study periods and at some time points, or only at some discrete time points. Therefore, we have combined recurrent event and panel count data. For the problem, we present a multiple imputation-based estimation procedure and one advantage of the proposed marginal model approach is that it can be easily implemented. To assess the performance of the procedure, a simulation study is conducted and indicates that it performs well for practical situations and can be more efficient than the existing method. The methodology is applied to a set of mixed data from a longitudinal cohort study.
本文讨论了使用比例率模型对事件史研究中出现的一类不完全混合数据进行回归分析。所谓混合数据，是指每个研究对象可能在整个研究期间持续被观察，在某些研究期间持续被观察且在某些时间点被观察，或者仅在某些离散时间点被观察。因此，我们将复发事件数据和面板计数数据结合在了一起。针对该问题，我们提出了一种基于多重填补的估计程序，所提出的边际模型方法的一个优点是它易于实施。为了评估该程序的性能，我们进行了一项模拟研究，结果表明它在实际情况下表现良好，并且可能比现有方法更有效。该方法应用于一组来自纵向队列研究的混合数据。
