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Hierarchical likelihood inference on clustered competing risks data.

Nicholas J Christian1, Il Do Ha2, Jong-Hyeon Jeong1

  • 1Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, U.S.A.

Statistics in Medicine
|August 18, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new hierarchical likelihood method to analyze clustered survival data with competing risks. This approach effectively models correlated underlying risks, improving analysis for complex health outcomes.

Keywords:
cause-specific hazardclustered datacompeting risksfrailty modelshierarchical likelihood

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Frailty models extend proportional hazards models for clustered survival data.
  • Standard frailty models are insufficient for clustered data with competing risks.
  • Competing risks may involve distinct yet correlated underlying processes.

Purpose of the Study:

  • To propose a novel hierarchical likelihood method for analyzing clustered competing risks data.
  • To develop a cause-specific hazard frailty model suitable for complex clustered data.
  • To provide a method that avoids intensive numerical computations for marginal distributions.

Main Methods:

  • The hierarchical likelihood method is introduced, integrating fixed and random effects.
  • This approach extends the standard likelihood function for clustered competing risks.
  • The method facilitates inference on cause-specific hazard frailty models.

Main Results:

  • Simulation studies evaluated the performance of estimators for regression coefficients.
  • The correlation structure within bivariate frailty distributions for competing events was assessed.
  • The proposed method demonstrated robust behavior in simulations.

Conclusions:

  • The hierarchical likelihood method offers a viable approach for clustered competing risks survival data.
  • The method effectively models correlated risks in complex clustered settings.
  • Application to a breast cancer dataset illustrates its practical utility.