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Related Experiment Videos

Correlated binary regression with covariates specific to each binary observation.

R L Prentice1

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98104.

Biometrics
|December 1, 1988
PubMed
Summary
This summary is machine-generated.

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This study explores regression methods for correlated binary data. A generalized estimating equation approach is recommended for analyzing response probabilities and correlations, outperforming complex parametric models.

Area of Science:

  • Statistics
  • Biostatistics
  • Correlated Data Analysis

Background:

  • Analysis of correlated binary data presents challenges, especially when observations have unique covariates.
  • Existing models that condition on some responses may not accurately estimate marginal probabilities or correlations.
  • Parametric methods for these estimations are often overly complex, except for specific cases like paired data.

Purpose of the Study:

  • To evaluate regression methods for correlated binary data.
  • To identify suitable approaches for estimating marginal response probabilities and correlations.
  • To propose an alternative to complex parametric models.

Main Methods:

  • Consideration of regression models for correlated binary outcomes.
  • Evaluation of models that condition on observed binary responses within blocks.

Related Experiment Videos

  • Application and advocacy of the generalized estimating equation (GEE) approach.

    • Main Results:

    • Models conditioning on responses are limited for marginal probability and correlation estimation.
    • Fully parametric methods are complex for general correlated binary data.
    • The generalized estimating equation approach provides a viable method for inference.

    Conclusions:

    • The generalized estimating equation approach is effective for analyzing correlated binary data.
    • This method offers a practical solution for estimating marginal response probabilities and correlations.
    • The study provides illustrations for both small and large block sizes.