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Association measures for clustered competing risks.

Chien-Lin Su1, Lajmi Lakhal-Chaieb2

  • 1Department of Mathematics and Statistics, McGill University, Montréal, Québec, Canada.

Statistics in Medicine
|December 5, 2019
PubMed
Summary
This summary is machine-generated.

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This study introduces a new statistical model for analyzing clustered competing risks data, improving understanding of event occurrences within groups. The method uses copula models to assess associations and offers a robust two-stage estimation procedure.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Survival Analysis

Background:

  • Multivariate clustered competing risks data present unique analytical challenges.
  • Understanding associations between different failure types within clusters is crucial.

Purpose of the Study:

  • To propose a semiparametric model for multivariate clustered competing risks data.
  • To investigate associations between cause-specific failure times and competing risk events within clusters.
  • To introduce a cross-odds ratio measure for quantifying these associations.

Main Methods:

  • Utilizing Cox proportional hazard models for cause-specific hazard functions.
  • Employing copula models to capture dependence structures within clusters.
  • Implementing a two-stage estimation procedure with an expectation-maximization algorithm.
Keywords:
cause-specific failure timescause-specific hazard functionscopulacox proportional hazard modelexpectation-maximization algorithm

Related Experiment Videos

Main Results:

  • The proposed estimators demonstrate consistency and asymptotic normality.
  • Simulation studies confirm the method's finite sample performance.
  • A cross-odds ratio measure is explored for association quantification.

Conclusions:

  • The developed semiparametric model effectively analyzes multivariate clustered competing risks data.
  • The two-stage estimation provides a reliable approach for parameter estimation.
  • The method is applicable to real-world datasets, such as Bone Marrow transplantation studies.