Chen Ling, Feng Yanqin, Sun Jianguo
Division of Biostatistics, Washington University School of Medicine, Campus Box 8067, 660 S. Euclid Ave, St. Louis, MO 63110, U.S.A.
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
Commun Stat Theory Methods. 2020;49(16):4030-4045. doi: 10.1080/03610926.2019.1594299. Epub 2019 Apr 3.
The gap time between recurrent events is often of primary interest in many fields such as medical studies (Cook and Lawless 2007; Kang, Sun, and Zhao 2015; Schaubel and Cai 2004), and in this paper, we discuss regression analysis of the gap times arising from a general class of additive transformation models. For the problem, we propose two estimation procedures, the modified within-cluster resampling (MWCR) method and the weighted risk-set (WRS) method, and the proposed estimators are shown to be consistent and asymptotically follow the normal distribution. In particular, the estimators have closed forms and can be easily determined, and the methods have the advantage of leaving the correlation among gap times arbitrary. A simulation study is conducted for assessing the finite sample performance of the presented methods and suggests that they work well in practical situations. Also the methods are applied to a set of real data from a chronic granulomatous disease (CGD) clinical trial.
复发事件之间的间隔时间在许多领域通常是主要关注的内容，比如医学研究（库克和劳利斯，2007；康、孙和赵，2015；绍贝尔和蔡，2004）。在本文中，我们讨论了一类一般的加法变换模型产生的间隔时间的回归分析。针对这个问题，我们提出了两种估计方法，即修正的聚类内重抽样（MWCR）方法和加权风险集（WRS）方法，并且所提出的估计量被证明是一致的，且渐近服从正态分布。特别地，这些估计量具有封闭形式并且能够容易地确定，而且这些方法具有使间隔时间之间的相关性任意的优点。进行了一项模拟研究来评估所提出方法的有限样本性能，结果表明它们在实际情况中效果良好。此外，这些方法还被应用于一组来自慢性肉芽肿病（CGD）临床试验的真实数据。
