A class of additive transformation models for recurrent gap times

Ling Chen, Yanqin Feng, Jianguo Sun

Research output: Contribution to journalArticlepeer-review


The gap time between recurrent events is often of primary interest in many fields such as medical studies, and in this article, 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.

Original languageEnglish
Pages (from-to)4030-4045
Number of pages16
JournalCommunications in Statistics - Theory and Methods
Issue number16
StatePublished - Aug 17 2020


  • Additive transformation model
  • gap times
  • latent variable
  • recurrent event data
  • within-cluster resampling


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