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Related Experiment Videos

Modeling multivariate discrete failure time data

J H Shih1

  • 1Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892-7938, USA. jshih@helix.nih.gov

Biometrics
|September 29, 1998
PubMed
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This study introduces a flexible bivariate discrete survival distribution for modeling paired data. The proposed method accurately estimates associations and marginal effects, showing high efficiency in simulations.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Traditional survival models often struggle with complex dependencies between paired or grouped outcomes.
  • Flexible modeling of marginal distributions and pairwise associations is crucial in various fields, including medical research.

Purpose of the Study:

  • To propose a novel bivariate discrete survival distribution with a constant odds ratio.
  • To develop a pseudo-likelihood estimation procedure for regression and association parameters.
  • To extend the methodology to multivariate settings and evaluate its performance via simulations.

Main Methods:

  • Development of a flexible bivariate discrete survival distribution.
  • Application of pseudo-likelihood estimation for marginal regression and pairwise odds ratio parameters.

Related Experiment Videos

  • Simulation studies to assess the efficiency and accuracy of the estimation procedure for both bivariate and multivariate data.

    • Main Results:

    • The proposed pseudo-likelihood estimation demonstrates high efficiency for association parameters in bivariate data.
    • Marginal regression coefficient estimates show minimal efficiency loss, especially with weaker associations.
    • Simulation results indicate consistent parameter estimates and coverage probabilities close to nominal levels for multivariate data.

    Conclusions:

    • The proposed bivariate discrete survival distribution and pseudo-likelihood estimation offer a robust framework for analyzing correlated survival data.
    • The methods are effective in capturing both marginal effects and pairwise associations, as demonstrated by simulations and application to sibling failure times.