Guan Yongtao
Division of Biostatistics, Yale University, New Haven, Connecticut 06520, USA.
Biometrics. 2011 Sep;67(3):730-9. doi: 10.1111/j.1541-0420.2011.01557.x. Epub 2011 Mar 1.
A typical recurrent event dataset consists of an often large number of recurrent event processes, each of which contains multiple event times observed from an individual during a follow-up period. Such data have become increasingly available in medical and epidemiological studies. In this article, we introduce novel procedures to conduct second-order analysis for a flexible class of semiparametric recurrent event processes. Such an analysis can provide useful information regarding the dependence structure within each recurrent event process. Specifically, we will use the proposed procedures to test whether the individual recurrent event processes are all Poisson processes and to suggest sensible alternative models for them if they are not. We apply these procedures to a well-known recurrent event dataset on chronic granulomatous disease and an epidemiological dataset on meningococcal disease cases in Merseyside, United Kingdom to illustrate their practical value.
一个典型的复发事件数据集通常由大量的复发事件过程组成，每个过程都包含在随访期内从个体观察到的多个事件发生时间。这类数据在医学和流行病学研究中越来越常见。在本文中，我们介绍了新颖的方法，用于对一类灵活的半参数复发事件过程进行二阶分析。这样的分析可以提供有关每个复发事件过程内依赖结构的有用信息。具体而言，我们将使用所提出的方法来检验各个复发事件过程是否均为泊松过程，并在不是泊松过程的情况下为它们提出合理的替代模型。我们将这些方法应用于一个关于慢性肉芽肿病的著名复发事件数据集以及英国默西塞德郡脑膜炎球菌病病例的流行病学数据集，以说明它们的实际价值。
