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Related Experiment Videos

Some recent developments for regression analysis of multivariate failure time data

K Y Liang1, S G Self, K J Bandeen-Roche

  • 1Department of Biostatistics, Johns Hopkins University, School of Hygiene and Public Health, Baltimore, MD 21205, USA.

Lifetime Data Analysis
|January 1, 1995
PubMed
Summary
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This paper surveys regression models for correlated failure times in clustered data, focusing on frailty and marginal models. These methods extend Cox

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Traditional regression methods assume independent observations.
  • Clustered data, such as from family members or bilateral organs, exhibit correlated failure times.
  • This violates the independence assumption critical for standard survival analysis.

Purpose of the Study:

  • To survey and present models for multivariate failure time data.
  • To discuss extensions of the proportional hazards model for correlated outcomes.
  • To cover model formulation, parameter interpretation, and estimation.

Main Methods:

  • Frailty models: Incorporate latent variables (frailties) to induce correlation within clusters.
  • Marginal models: Model marginal distributions and associations separately.

Related Experiment Videos

  • Focus on extensions of the proportional hazards model for multivariate data.

    • Main Results:

    • Presents two distinct classes of models: frailty and marginal models.
    • Details regression models for conditional distributions (frailty) and marginal distributions (marginal).
    • Considers recent extensions of proportional hazards models for correlated survival data.

    Conclusions:

    • Frailty and marginal models offer frameworks for analyzing correlated failure time data.
    • These models address limitations of traditional methods when independence is violated.
    • The survey covers key aspects of model formulation, interpretation, and estimation for multivariate survival data.