Hsieh Jin-Jian, Ding A Adam, Wang Weijing
Department of Mathematics, National Chung Cheng University, Chia-Yi, Taiwan, Republic of China.
Biometrics. 2011 Sep;67(3):719-29. doi: 10.1111/j.1541-0420.2010.01497.x. Epub 2010 Oct 29.
Recurrent events data are commonly seen in longitudinal follow-up studies. Dependent censoring often occurs due to death or exclusion from the study related to the disease process. In this article, we assume flexible marginal regression models on the recurrence process and the dependent censoring time without specifying their dependence structure. The proposed model generalizes the approach by Ghosh and Lin (2003, Biometrics 59, 877-885). The technique of artificial censoring provides a way to maintain the homogeneity of the hypothetical error variables under dependent censoring. Here we propose to apply this technique to two Gehan-type statistics. One considers only order information for pairs whereas the other utilizes additional information of observed censoring times available for recurrence data. A model-checking procedure is also proposed to assess the adequacy of the fitted model. The proposed estimators have good asymptotic properties. Their finite-sample performances are examined via simulations. Finally, the proposed methods are applied to analyze the AIDS linked to the intravenous experiences cohort data.
复发事件数据在纵向随访研究中很常见。由于与疾病过程相关的死亡或被排除在研究之外，常出现相依删失。在本文中，我们在不指定复发过程和相依删失时间的依赖结构的情况下，对其假设灵活的边际回归模型。所提出的模型推广了Ghosh和Lin（2003年，《生物统计学》59卷，877 - 885页）的方法。人工删失技术提供了一种在相依删失下保持假设误差变量同质性的方法。在此我们提议将该技术应用于两个Gehan型统计量。一个仅考虑配对的顺序信息，而另一个利用复发数据中可用的观察删失时间的附加信息。还提出了一个模型检验程序来评估拟合模型的充分性。所提出的估计量具有良好的渐近性质。通过模拟检验了它们的有限样本性能。最后，将所提出的方法应用于分析与静脉注射经历队列数据相关的艾滋病。
