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Working covariance model selection for generalized estimating equations.

Vincent J Carey1, You-Gan Wang

  • 1Harvard Medical School, Channing Laboratory, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, USA. stvjc@channing.harvard.edu

Statistics in Medicine
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Summary
This summary is machine-generated.

This study introduces methods for selecting covariance models in generalized estimating equations for correlated data. Gaussian pseudolikelihood shows promise in simulations for longitudinal data analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Generalized estimating equations (GEE) are widely used for analyzing correlated data.
  • Accurate covariance model selection is crucial for valid inference in GEE.
  • Current methods for covariance model selection may lack sensitivity for various data structures.

Purpose of the Study:

  • To investigate and compare data-based methods for selecting working covariance models in GEE.
  • To evaluate the performance of Gaussian pseudolikelihood and a geodesic distance criterion.
  • To provide practical tools for assessing covariance model adequacy in longitudinal studies.

Main Methods:

  • Simulation studies were conducted using various response distributions and mean-variance relationships.
  • Two selection criteria were evaluated: Gaussian pseudolikelihood and geodesic distance.
  • The methods were applied to a real-world clinical dataset.

Main Results:

  • Gaussian pseudolikelihood demonstrated reasonable sensitivity across different response distributions and noncanonical variance functions.
  • The geodesic distance criterion also showed utility in model selection.
  • The study highlights the importance of assessing both correlation and variance models.

Conclusions:

  • Data-based covariance model selection is essential for robust GEE analyses.
  • Gaussian pseudolikelihood is a viable and sensitive method for this purpose.
  • Open-source software is available to support routine assessment of covariance models in longitudinal data.