A0599
Title: A class of additive transformation models for recurrent gap times
Authors: Ling Chen - Washington University in St. Louis (United States) [presenting]
Yanqin Feng - School of Mathematics and Statistics Wuhan University (China)
Jianguo Sun - University of Missouri (United States)
Abstract: The gap time between recurrent events is often of primary interest in many fields such as medical studies. 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.
