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Related Experiment Videos

Marginalized binary mixed-effects models with covariate-dependent random effects and likelihood inference.

Zengri Wang1, Thomas A Louis

  • 1Medtronic Inc., Saint Paul, Minnesota 55126, USA. zengri.wang@medtronic.com

Biometrics
|December 21, 2004
PubMed
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This study introduces a unified approach for analyzing clustered binary data by matching conditional and marginal models. This method improves regression parameter and prediction interpretation in generalized linear mixed models.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Clustered binary data analysis commonly employs marginal and conditional mixed-effects models.
  • Nonlinear mixed-effects models often lack direct marginal interpretation for parameters and predictions.
  • A unified approach is desirable for simultaneous marginal and conditional inference.

Purpose of the Study:

  • To develop a generalized linear mixed model parameterization that unifies marginal and conditional inference for clustered binary data.
  • To address the interpretational challenges of nonlinear mixed-effects models by matching conditional and marginal functional forms.
  • To enable robust analysis of clustered binary outcomes where both population-averaged and subject-specific effects are of interest.

Main Methods:

Related Experiment Videos

  • Investigated a generalized linear mixed model with a structured random-intercept distribution.
  • Modeled the marginal mean of the response distribution.
  • Selected the random intercept distribution to align conditional and marginal shapes and model covariate-dependent random effects.

    • Main Results:

    • Developed a novel parameterization for generalized linear mixed models.
    • Demonstrated the ability to match conditional and marginal model shapes.
    • Successfully modeled covariate-dependent random effects.

    Conclusions:

    • The proposed unified approach offers a valuable tool for analyzing clustered binary data.
    • This method enhances the interpretability of regression parameters and predictions in mixed-effects models.
    • The approach was validated on two real-world datasets, showing its practical applicability.