Second-Order Analysis of Semiparametric Recurrent Event Processes

Research output: Contribution to journalArticle

Abstract

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
JournalBiometrics
Volume67
Issue number3
DOIs
StatePublished - Sep 2011
Externally publishedYes

Fingerprint

Recurrent Events
chronic diseases
epidemiological studies
United Kingdom
Chronic Granulomatous Disease
testing
Epidemiologic Studies
Chronic Disease
Dependence Structure
Poisson process
Datasets
Alternatives

Keywords

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

ASJC Scopus subject areas

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

Cite this

Second-Order Analysis of Semiparametric Recurrent Event Processes. / Guan, Yongtao.

In: Biometrics, Vol. 67, No. 3, 09.2011, p. 730-739.

Research output: Contribution to journalArticle

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