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REML estimation for survival models with frailty

C A McGilchrist1

  • 1Fred Hutchinson Cancer Research Center, Seattle, Washington 98107.

Biometrics
|March 1, 1993
PubMed
Summary

This study applies generalized mixed models to estimate regression parameters in proportional hazards models with repeated failure times. It incorporates subject effects as random frailty terms for improved accuracy in survival analysis.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Proportional hazards models are widely used for analyzing time-to-event data.
  • Repeated failure observations within subjects present unique statistical challenges.
  • Existing methods may not fully account for individual subject variability.

Purpose of the Study:

  • To estimate regression parameters in proportional hazards models with repeated failure observations.
  • To incorporate subject-specific effects using a random frailty term.
  • To enhance the accuracy of survival data analysis.

Main Methods:

  • Application of a generalized mixed model estimation method.
  • Inclusion of subject effect as a random frailty term.
  • Utilizing best linear unbiased predictors (BLUPs) for initial estimation.
  • Computation of maximum likelihood (ML) and restricted maximum likelihood (REML) estimates.

Main Results:

  • Successful estimation of regression parameters in complex survival data.
  • Demonstration of the utility of random frailty terms for subject effects.
  • Validation of BLUPs as an effective initial step for ML/REML estimation.

Conclusions:

  • The proposed method provides a robust approach for survival analysis with repeated measures.
  • Incorporating random frailty terms improves the modeling of individual heterogeneity.
  • This methodology offers advancements in statistical estimation for time-to-event data.

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