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Related Experiment Videos

Empirical Bayes estimation and inference for the random effects model with binary response

M A Waclawiw1, K Y Liang

  • 1Biostatistics Research Branch, National Heart, Lung and Blood Institute, Bethesda, Md. 20892.

Statistics in Medicine
|March 15, 1994
PubMed
Summary
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This study applies an estimating function approach to analyze binary longitudinal data, proposing a novel bootstrapping method for empirical Bayes confidence intervals in complex models.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Trial Methodology

Background:

  • Classical two-stage random effects models are common for longitudinal data.
  • Parametric models with binary responses often present analytically intractable likelihoods.
  • Existing methods may not adequately address complex longitudinal data structures.

Purpose of the Study:

  • To adapt and apply an estimating function-based approach for binary longitudinal data.
  • To develop a fully parametric bootstrapping method for parameter estimation and inference.
  • To provide empirical Bayes confidence intervals for model parameters in intractable likelihood settings.

Main Methods:

  • Utilized an estimating function framework for parameter estimation.

Related Experiment Videos

  • Implemented a fully parametric bootstrapping technique.
  • Applied the methodology to a binary response setting with fixed and univariate random effects.
  • Leveraged developments from Laird and Louis for the bootstrapping approach.

    • Main Results:

    • Demonstrated successful convergence of the proposed algorithm.
    • Generated empirical Bayes confidence intervals for model parameters.
    • Successfully applied the methodology to a crossover clinical trial with binary responses.

    Conclusions:

    • The estimating function approach offers a robust framework for analyzing diverse medical data, including longitudinal binary outcomes.
    • The proposed bootstrapping method effectively derives confidence intervals for complex models.
    • This methodology is particularly useful for small and varying numbers of discrete repeated observations.