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Regression estimation using multivariate failure time data and a common baseline hazard function model

J Cai1, R L Prentice

  • 1University of North Carolina, Chapel Hill, USA.

Lifetime Data Analysis
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

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This study introduces a new weighted partial likelihood method for analyzing multivariate failure time data with a common baseline hazard. Simulations show this approach improves efficiency, especially with strong cluster dependencies.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Marginal methods for multivariate failure time data often assume distinct baseline hazards.
  • A common baseline hazard is more appropriate for studies like family-based genetic epidemiology and group randomized trials.

Purpose of the Study:

  • To develop and evaluate a weighted partial likelihood score equation for regression parameter estimation under a common baseline hazard model.
  • To assess the asymptotic distribution theory for this new method.

Main Methods:

  • Utilized a weighted partial likelihood score equation for parameter estimation.
  • Developed asymptotic distribution theory for the proposed method.
  • Conducted extensive simulation studies to evaluate performance.

Related Experiment Videos

Main Results:

  • The proposed method provides adequate asymptotic distributional approximations.
  • Weighting offers significant efficiency gains, particularly with strong within-cluster dependency and larger cluster sizes.

Conclusions:

  • The weighted partial likelihood approach is a viable and efficient method for multivariate failure time data with a common baseline hazard.
  • The findings are relevant for genetic epidemiology and intervention trial analyses.