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Related Experiment Videos

Assessing gamma frailty models for clustered failure time data

J H Shih1, T A Louis

  • 1Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892, USA.

Lifetime Data Analysis
|January 1, 1995
PubMed
Summary
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This study introduces a new graphical method to check if proportional hazards frailty models fit clustered failure time data, specifically assessing the gamma distribution assumption for frailties.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Proportional hazards frailty models are used for clustered failure time data.
  • These models typically assume a gamma distribution for the random frailty effect.
  • Assessing the adequacy of this gamma distribution assumption is crucial for model validity.

Purpose of the Study:

  • To propose a novel graphical method for assessing the adequacy of proportional hazards frailty models.
  • To specifically evaluate the goodness of fit for the gamma distribution assumption of frailties.
  • To provide a practical tool for researchers using these models.

Main Methods:

  • Calculating the average of posterior expected frailties at multiple follow-up time points.
  • Comparing these calculated averages to the known mean frailty (1).

Related Experiment Videos

  • Deriving and estimating the standard error for the mean of posterior expected frailties to aid interpretation.

    • Main Results:

    • Large discrepancies between the calculated average frailties and the known mean indicate a lack of model fit.
    • The proposed method provides a visual assessment of model adequacy.
    • Sensitivity analysis through simulations supports the methodology's robustness.

    Conclusions:

    • The proposed graphical method effectively assesses the adequacy of proportional hazards frailty models, particularly the gamma distribution assumption.
    • This approach aids in identifying potential model misspecification.
    • The methodology is illustrated with an example and validated via simulations.