Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, U.S.A.
Stat Med. 2011 Feb 20;30(4):301-11. doi: 10.1002/sim.4074.
The gap times between recurrent events are often of primary interest in medical and epidemiology studies. The observed gap times cannot be naively treated as clustered survival data in analysis because of the sequential structure of recurrent events. This paper introduces two important building blocks, the averaged counting process and the averaged at-risk process, for the development of the weighted risk-set (WRS) estimation methods. We demonstrate that with the use of these two empirical processes, existing risk-set based methods for univariate survival time data can be easily extended to analyze recurrent gap times. Additionally, we propose a modified within-cluster resampling (MWCR) method that can be easily implemented in standard software. We show that the MWCR estimators are asymptotically equivalent to the WRS estimators. An analysis of hospitalization data from the Danish Psychiatric Central Register is presented to illustrate the proposed methods.
在医学和流行病学研究中，常常主要关注复发事件之间的间隔时间。由于复发事件的顺序结构，观察到的间隔时间在分析中不能简单地视为聚类生存数据。本文介绍了两个重要的构建块，即平均计数过程和平均风险过程，用于开发加权风险集（WRS）估计方法。我们证明，通过使用这两个经验过程，可以轻松地将现有的基于风险集的方法扩展到分析复发间隔时间。此外，我们提出了一种改进的聚类内重采样（MWCR）方法，该方法可以在标准软件中轻松实现。我们表明，MWCR 估计量与 WRS 估计量在渐近意义上是等效的。本文还通过丹麦精神病学中央登记处的住院数据进行了分析，以说明所提出的方法。
