Semiparametric regression analysis for alternating recurrent event data

Chi Hyun Lee, Chiung Yu Huang, Gongjun Xu, Xianghua Luo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Alternating recurrent event data arise frequently in clinical and epidemiologic studies, where 2 types of events such as hospital admission and discharge occur alternately over time. The 2 alternating states defined by these recurrent events could each carry important and distinct information about a patient's underlying health condition and/or the quality of care. In this paper, we propose a semiparametric method for evaluating covariate effects on the 2 alternating states jointly. The proposed methodology accounts for the dependence among the alternating states as well as the heterogeneity across patients via a frailty with unspecified distribution. Moreover, the estimation procedure, which is based on smooth estimating equations, not only properly addresses challenges such as induced dependent censoring and intercept sampling bias commonly confronted in serial event gap time data but also is more computationally tractable than the existing rank-based methods. The proposed methods are evaluated by simulation studies and illustrated by analyzing psychiatric contacts from the South Verona Psychiatric Case Register.

Original languageEnglish (US)
Pages (from-to)996-1008
Number of pages13
JournalStatistics in Medicine
Issue number6
StatePublished - Mar 15 2018
Externally publishedYes


  • accelerated failure time model
  • alternating renewal process
  • gap times
  • recurrent events

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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