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

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  • Jing Ning
  • Yong Chen
  • Chunyan Cai
  • Xuelin Huang
  • Mei-Cheng Wang

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.

Suggested Citation

  • Jing Ning & Yong Chen & Chunyan Cai & Xuelin Huang & Mei-Cheng Wang, 2015. "On the dependence structure of bivariate recurrent event processes: inference and estimation," Biometrika, Biometrika Trust, vol. 102(2), pages 345-358.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:2:p:345-358.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu073
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    Cited by:

    1. Jing Ning & Chunyan Cai & Yong Chen & Xuelin Huang & Mei‐Cheng Wang, 2020. "Semiparametric modelling and estimation of covariate‐adjusted dependence between bivariate recurrent events," Biometrics, The International Biometric Society, vol. 76(4), pages 1229-1239, December.

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