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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data.

Chiung-Yu Huang1, Mei-Cheng Wang

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 ( cyhuang@biostat.umn.edu ).

Journal of the American Statistical Association
|September 27, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible joint model for recurrent events and failure times, allowing for informative censoring. The "borrow-strength" method effectively estimates associations, improving analysis in longitudinal studies.

Keywords:
Borrow-strength methodFrailtyInformative censoringJoint modelNonstationary Poisson process

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Recurrent event data are crucial in biomedical and demographic studies for tracking disease progression and risk.
  • Standard analysis methods often assume independent censoring, which may be violated when observation times correlate with event processes.
  • Informative censoring, where censoring depends on the event process, poses a challenge in analyzing such data.

Purpose of the Study:

  • To develop a flexible joint model for analyzing recurrent event processes and failure times.
  • To account for the association between recurrent events and the time to a terminal event, even with informative censoring.
  • To propose a novel estimation procedure that accommodates subject-specific latent variables.

Main Methods:

  • A joint modeling approach is proposed, linking recurrent event intensity and failure time hazard via a shared subject-specific latent variable.
  • The model accommodates informative censoring without requiring parametric assumptions on censoring times or latent variables.
  • A "borrow-strength estimation procedure" is introduced, utilizing estimated latent variable values for both processes.

Main Results:

  • The proposed model allows for informative censoring in both recurrent event and failure time data.
  • The "borrow-strength" method provides a way to estimate the association between recurrent events and failure times.
  • The study investigates the properties of regression parameter estimates and cumulative hazard functions under the proposed model.

Conclusions:

  • The developed joint model offers a flexible framework for analyzing complex longitudinal data with recurrent events and potential informative censoring.
  • The "borrow-strength" estimation procedure is effective for handling the association between recurrent events and failure times.
  • This approach enhances the statistical analysis of disease progression and risk assessment in longitudinal studies.