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

Ye Shen1, Hui Huang2, Yongtao Guan3

  • 1Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA, U.S.A.. yeshen@uga.edu.

Statistics in Medicine
|June 1, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing recurrent event data with longitudinal markers, improving accuracy in biomedical research. The approach properly incorporates longitudinal information, reducing bias in recurrent event analysis.

Keywords:
estimating equationjoint modelinglongitudinal datarandom effectrecurrent event data

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Recurrent event data are prevalent in biomedical and epidemiological studies.
  • Often, this data includes longitudinal information on surrogate markers.
  • Existing methods using Cox models with time-dependent covariates can yield biased results, particularly with measurement error in longitudinal outcomes.

Purpose of the Study:

  • To develop a robust statistical approach for analyzing recurrent event data that properly incorporates longitudinal information.
  • To address the bias issues associated with traditional methods when longitudinal outcomes are measured with error.
  • To provide a reliable method for modeling the correlation between longitudinal and recurrent event processes.

Main Methods:

  • Modeling the correlation between longitudinal and recurrent event processes using latent random effects.
  • Proposing a two-stage conditional estimating equation approach.
  • Estimating the rate function of the recurrent event process conditioned on observed longitudinal data.

Main Results:

  • Simulation studies demonstrated the performance of the proposed approach.
  • The method was applied to analyze cocaine addiction data, including relapse events and craving scores.
  • The approach effectively models the interplay between longitudinal craving and relapse events.

Conclusions:

  • The proposed two-stage conditional estimating equation approach provides a valid method for analyzing recurrent event data with longitudinal markers.
  • This method offers an improvement over traditional approaches, particularly when longitudinal data is subject to error.
  • The application to cocaine addiction data highlights its utility in real-world biomedical research.