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A marginal mixed baseline hazards model for multivariate failure time data.

L X Clegg1, J Cai, P K Sen

  • 1National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892-7352, USA. lin_clegg@nih.gov

Biometrics
|April 21, 2001
PubMed
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This study introduces a new marginal mixed baseline hazards model for analyzing multiple correlated failure times without assuming dependence structures. The method provides consistent estimators for hazard ratios, validated by simulations and real-world data.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Multivariate Data Analysis

Background:

  • Marginal regression models are preferred in multivariate failure time analysis to avoid complex dependence structure assumptions.
  • Correlated failure times require specialized statistical methods for accurate analysis.

Purpose of the Study:

  • To introduce a novel marginal mixed baseline hazards model for multivariate failure time data.
  • To develop and validate methods for estimating marginal hazard ratio parameters.

Main Methods:

  • Development of estimating equations for parameter estimation.
  • Theoretical analysis showing consistency and asymptotic Gaussian properties of estimators.
  • Robust covariance matrix estimation.

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Main Results:

  • Proposed estimators are consistent and asymptotically Gaussian.
  • A robust covariance matrix can be consistently estimated.
  • Simulation studies confirm the methodology's adequacy for practical sample sizes.

Conclusions:

  • The proposed marginal mixed baseline hazards model offers a robust approach for multivariate failure time analysis.
  • The methodology is suitable for real-world applications, as demonstrated by the Framingham Heart Study data.