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Non-parametric methods for recurrent event data with informative and non-informative censorings.

Mei-Cheng Wang1, Chin-Tsang Chiang

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. mcwang@jhsph.edu

Statistics in Medicine
|January 29, 2002
PubMed
Summary

This study addresses recurrent event data analysis in longitudinal health studies, comparing risk set methods with robust non-parametric approaches for informative censoring. The research details methods for estimating cumulative occurrence rate and occurrence rate functions.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Recurrent event data are prevalent in health studies.
  • Risk set methods are standard for non-informative censoring but yield biased results with informative censoring.

Purpose of the Study:

  • To investigate time-to-events models for recurrent event data.
  • To compare standard risk set methods with non-parametric approaches robust to informative censoring.

Main Methods:

  • Evaluation of risk set methods for recurrent event data.
  • Development and discussion of non-parametric procedures for estimating cumulative occurrence rate function (CORF) and occurrence rate function (ORF).
  • Comparative analysis using simulation and real-world data from the AIDS Link to Intravenous Experiences Cohort Study.

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Main Results:

  • Risk set methods are efficient for non-informative censoring but produce biased estimates when censoring is informative.
  • Non-parametric approaches demonstrate robustness under informative censoring scenarios.

Conclusions:

  • Non-parametric methods are recommended for analyzing recurrent event data when informative censoring is present.
  • Accurate estimation of CORF and ORF is crucial for understanding recurrent event processes under complex censoring conditions.