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Marginal means/rates models for multiple type recurrent event data.

Jianwen Cai1, Douglas E Schaubel

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, USA. cai@bios.unc.edu

Lifetime Data Analysis
|August 6, 2004
PubMed
Summary
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This study introduces new statistical models for analyzing recurrent and multiple health events, even with incomplete patient data. These methods help understand risk factors for conditions like preschool asthma.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Research

Background:

  • Recurrent events are common in biomedical research, often with multiple event types of interest.
  • Censored follow-up time (due to loss or administrative reasons) is a frequent challenge in these studies.

Purpose of the Study:

  • To propose semi-parametric marginal models for analyzing censored recurrent event data.
  • To assess the impact of covariates on censored event processes using a relative risk framework.

Main Methods:

  • Development of a class of semi-parametric marginal means/rates models.
  • Formulation of estimating equations for model parameter estimation.
  • Examination of asymptotic properties of parameter estimators and finite sample properties via simulations.

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Main Results:

  • The proposed models provide a robust framework for analyzing complex event data.
  • Simulation studies confirmed the properties of the regression coefficient estimators.
  • Application to a real-world dataset identified risk factors for preschool asthma.

Conclusions:

  • The developed semi-parametric models are effective for studying censored recurrent events in biomedical research.
  • These methods offer valuable tools for covariate analysis in the presence of censoring.
  • The approach is applicable to various health outcomes, including childhood asthma risk.