Online Program Home
My Program

Abstract Details

Activity Number: 409 - Survival Analysis I
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #328753
Title: A Class of Additive Transformation Models for Recurrent Gap Times
Author(s): Ling Chen* and Yanqin Feng and (Tony) Jianguo Sun
Companies: Washington University in St. Louis and School of Mathematics and Statistics, Wuhan University and University of Missouri
Keywords: additive transformation model; recurrent event data; gap times; within-cluster resampling; latent variable

The gap time between recurrent events is often of primary interest in many ?elds such as medical studies (Cook and Lawless, 2007; Kang et al., 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 modi?ed within-cluster resampling (MWCR) method and the weighted risk-set (WRS) method, and the proposed estimators are consistent and asymptotically follow the normal distribution. In particular, the estimators have closed forms and can be easily determined. A simulation study is conducted for assessing the ?nite 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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program