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 Citations (Scopus)

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

Fingerprint

Recurrent Events
Estimating Equation
Cocaine
Cocaine-Related Disorders
Proportional Hazards Models
Epidemiologic Studies
Biomarkers
Recurrence
Health
Surrogate Markers
Latent Process
Time-dependent Covariates
Cox Model
Rate Function
Random Effects
Biased
Term
Craving
Model

Keywords

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

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

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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.",
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AB - 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.

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