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

Competing risks regression for clustered data.

Bingqing Zhou1, Jason Fine, Aurelien Latouche

  • 1Division of Biostatistics, School of Public Health, Yale University, New Haven, CT 06520, USA. bingqing.zhou@yale.edu

Biostatistics (Oxford, England)
|November 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a population average regression model to analyze competing risks data with clustered individuals. The method accounts for within-cluster dependence, extending existing models for improved marginal effect estimation.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Clustered data in competing risks settings present unique analytical challenges.
  • Existing models like the Fine-Gray proportional hazards model may not fully capture within-cluster dependence.
  • Unobserved shared factors can induce correlation among individuals within a cluster, biasing results.

Purpose of the Study:

  • To propose a population average regression model for marginal effects in clustered competing risks data.
  • To extend the Fine-Gray model to accommodate within-cluster dependence.
  • To provide a robust method for estimating regression parameters without specifying the exact form of dependence.

Main Methods:

  • Development of estimators for regression parameters in the marginal model.
  • Utilizing an independence working assumption to handle unspecified within-cluster correlation.
  • Employing methods for consistent and asymptotically normal estimation.
  • Variance estimation without assuming a specific dependence structure.

Main Results:

  • The proposed estimators are consistent and asymptotically normal.
  • Variance estimation is feasible irrespective of the dependence structure within clusters.
  • A simulation study confirmed the procedures' efficacy with practical sample sizes.
  • The method demonstrated practical utility using European Bone Marrow Transplant Registry data.

Conclusions:

  • The population average regression model effectively addresses marginal effects in clustered competing risks data.
  • The method provides a flexible and robust approach for analyzing correlated survival data.
  • This extends the applicability of regression modeling in complex epidemiological and clinical settings.