Ghosh Debashis
Department of Statistics and Public Health Sciences, Penn State University, 514A Wartik Lab, University Park, PA 16802, USA.
Lifetime Data Anal. 2010 Oct;16(4):509-24. doi: 10.1007/s10985-009-9150-4. Epub 2010 Jan 10.
The analysis of recurrent failure time data from longitudinal studies can be complicated by the presence of dependent censoring. There has been a substantive literature that has developed based on an artificial censoring device. We explore in this article the connection between this class of methods with truncated data structures. In addition, a new procedure is developed for estimation and inference in a joint model for recurrent events and dependent censoring. Estimation proceeds using a mixed U-statistic based estimating function approach. New resampling-based methods for variance estimation and model checking are also described. The methods are illustrated by application to data from an HIV clinical trial as with a limited simulation study.
纵向研究中复发失败时间数据的分析可能会因存在相依删失而变得复杂。基于一种人为删失机制已经形成了大量文献。在本文中，我们探讨了这类方法与截断数据结构之间的联系。此外，还开发了一种新的程序，用于在复发事件和相依删失的联合模型中进行估计和推断。估计采用基于混合U统计量的估计函数方法。还描述了用于方差估计和模型检验的基于重抽样的新方法。通过应用于一项HIV临床试验的数据以及有限的模拟研究来说明这些方法。
