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

Cumulative Incidence Association Models for Bivariate Competing Risks Data.

Yu Cheng1, Jason P Fine

  • 1Department of Statistics and Department of Psychiatry, University of Pittsburgh Pittsburgh, PA, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|April 17, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces new copula models for analyzing bivariate competing risks, addressing noninformative censoring in survival data. The methods accurately estimate associations, showing promise for dementia research.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Clustered survival data analysis commonly employs association models like frailty and copula models.
  • The assumption of noninformative censoring, often applied, may not hold true in real-world scenarios.
  • Bivariate competing risk data presents unique challenges for modeling within-cluster associations.

Purpose of the Study:

  • To propose novel copula models for bivariate competing risk data, focusing on the bivariate cumulative incidence function (CIF).
  • To develop methods for estimating the association parameter under potential noninformative censoring.
  • To provide goodness-of-fit tests for evaluating parametric models within this framework.

Main Methods:

  • Copula models were adapted to relate bivariate CIFs to univariate CIFs, accommodating frailty.
  • Two estimating equations were developed for the association parameter, allowing flexible estimation of univariate CIFs (parametric or nonparametric).
  • Goodness-of-fit tests were introduced for formal model evaluation.

Main Results:

  • The proposed copula models effectively handle bivariate competing risk data and within-cluster associations.
  • Simulation studies demonstrated good performance of the estimators with moderate sample sizes.
  • The methodology was successfully applied to analyze dementia associations.

Conclusions:

  • The developed copula-based association models offer a robust approach for bivariate competing risk survival data.
  • The methods are valuable even when the noninformative censoring assumption is violated.
  • The approach is practical and applicable to complex health outcomes like dementia.