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Nonparametric Estimation of a Recurrent Survival Function.

Mei-Cheng Wang1, Shu-Hui Chang

  • 1Department of Biostatistics, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD 21205.

Journal of the American Statistical Association
|November 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces new nonparametric estimators for analyzing recurrence time data, crucial for longitudinal studies. These methods accurately estimate marginal survival functions, outperforming standard approaches for correlated survival data.

Keywords:
Correlated survival dataFrailtyKaplan-Meier estimateLongitudinal designsRecurrent event

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Longitudinal Data Analysis

Background:

  • Recurrent event data are common in longitudinal studies.
  • Recurrence times are a type of correlated survival data.
  • Standard methods may not apply due to the ordinal nature of recurrence times.

Purpose of the Study:

  • To develop and validate nonparametric estimators for marginal survival functions with recurrence time data.
  • To address limitations of existing methods like the Kaplan-Meier estimator for this data type.

Main Methods:

  • Introduction of a novel class of nonparametric estimators.
  • Theoretical validation of the proposed estimators.
  • Performance evaluation through simulations and real-world data analysis.

Main Results:

  • The proposed nonparametric estimators provide consistent estimates for the marginal survival function.
  • Simulations demonstrate the effectiveness of the new estimators.
  • Analysis of schizophrenia data illustrates practical application.

Conclusions:

  • The developed nonparametric estimators are appropriate for analyzing recurrence time data.
  • These methods offer an improvement over standard techniques for correlated survival data in specific contexts.