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Analyzing Recurrent Event Data With Informative Censoring.

Mei-Cheng Wang1, Jing Qin, Chin-Tsang Chiang

  • 1Department of Biostatistics, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD 21205 ( mcwang@jhsph.edu ).

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Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing recurrent event data, even when censoring might be informative. It models events using a Poisson process and a multiplicative intensity model, handling complex data effectively.

Keywords:
FrailtyIntensity functionLatent variableProportional rate modelRate function

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Recurrent event data analysis often assumes noninformative censoring, which is frequently unrealistic in practice.
  • Informative dropouts or failure events can compromise the independence assumption in longitudinal studies.
  • Existing methods may be inadequate when censoring mechanisms are dependent on the recurrent event process.

Purpose of the Study:

  • To develop statistical methods for analyzing recurrent event data with possibly informative censoring.
  • To model recurrent events using a subject-specific nonstationary Poisson process and a multiplicative intensity model.
  • To extend the multiplicative intensity model to incorporate covariate information for regression analysis.

Main Methods:

  • Modeling recurrent events with a subject-specific nonstationary Poisson process via a latent variable.
  • Employing a multiplicative intensity model for nonparametric estimation of the cumulative rate function.
  • Developing regression models that account for covariate information and treat censoring and latent variable distributions as nuisance parameters.

Main Results:

  • The proposed methods allow for the estimation of the cumulative rate function and regression parameters under informative censoring.
  • The approach effectively handles situations where censoring is not independent of the recurrent event process.
  • Demonstrated the utility of the methods through an analysis of the AIDS Link to Intravenous Experiences cohort data.

Conclusions:

  • The developed statistical framework provides a robust approach for analyzing recurrent event data when censoring may be informative.
  • By treating nuisance parameters appropriately, the methods avoid complex modeling and estimation of these distributions.
  • The application to real-world cohort data validates the practical applicability and effectiveness of the proposed techniques.