Second-Order Analysis of Semiparametric Recurrent Event Processes

Research output: Contribution to journalArticlepeer-review


A typical recurrent event dataset consists of an often large number of recurrent event processes, each of which contains multiple event times observed from an individual during a follow-up period. Such data have become increasingly available in medical and epidemiological studies. In this article, we introduce novel procedures to conduct second-order analysis for a flexible class of semiparametric recurrent event processes. Such an analysis can provide useful information regarding the dependence structure within each recurrent event process. Specifically, we will use the proposed procedures to test whether the individual recurrent event processes are all Poisson processes and to suggest sensible alternative models for them if they are not. We apply these procedures to a well-known recurrent event dataset on chronic granulomatous disease and an epidemiological dataset on meningococcal disease cases in Merseyside, United Kingdom to illustrate their practical value.

Original languageEnglish (US)
Pages (from-to)730-739
Number of pages10
Issue number3
StatePublished - Sep 2011
Externally publishedYes


  • Pair correlation function
  • Recurrent event process
  • Second-order analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


Dive into the research topics of 'Second-Order Analysis of Semiparametric Recurrent Event Processes'. Together they form a unique fingerprint.

Cite this