On the dependence structure of bivariate recurrent event processes: Inference and estimation

Jing Ning, Yong Chen, Chunyan Cai, Xuelin Huang, Mei Cheng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Bivariate or multivariate recurrent event processes are often encountered in longitudinal studies in which more than one type of event is of interest. There has been much research on regression analysis for such data, but little has been done to measure the dependence between recurrent event processes.We propose a time-dependent measure, termed the rate ratio, to assess the local dependence between two types of recurrent event processes. We model the rate ratio as a parametric function of time, and leave unspecified all other aspects of the distribution. We develop a composite likelihood procedure for model fitting and parameter estimation. We show that the proposed estimator is consistent and asymptotically normal. Its finite sample performance is evaluated by simulation and illustrated by an application to a soft tissue sarcoma study.

Original languageEnglish (US)
Pages (from-to)345-358
Number of pages14
JournalBiometrika
Volume102
Issue number2
DOIs
StatePublished - Jun 2015

Keywords

  • Bivariate recurrent event
  • Composite likelihood
  • Dependence measure
  • Multiple type recurrent event

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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