A conditional estimating equation approach for recurrent event data with additional longitudinal information

Ye Shen, Hui Huang, Yongtao Guan

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Recurrent event data are quite common in biomedical and epidemiological studies. A significant portion of these data also contain additional longitudinal information on surrogate markers. Previous studies have shown that popular methods using a Cox model with longitudinal outcomes as time-dependent covariates may lead to biased results, especially when longitudinal outcomes are measured with error. Hence, it is important to incorporate longitudinal information into the analysis properly. To achieve this, we model the correlation between longitudinal and recurrent event processes using latent random effect terms. We then propose a two-stage conditional estimating equation approach to model the rate function of recurrent event process conditioned on the observed longitudinal information. The performance of our proposed approach is evaluated through simulation. We also apply the approach to analyze cocaine addiction data collected by the University of Connecticut Health Center. The data include recurrent event information on cocaine relapse and longitudinal cocaine craving scores.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2016

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Keywords

  • Estimating equation
  • Joint modeling
  • Longitudinal data
  • Random effect
  • Recurrent event data

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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