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A determinant-based criterion for working correlation structure selection in generalized estimating equations.

Ajmery Jaman1, Mahbub A H M Latif1, Wasimul Bari2

  • 1Institute of Statistical Research and Training (ISRT), University of Dhaka, Dhaka-1000, Bangladesh.

Statistics in Medicine
|December 3, 2015
PubMed
Summary
This summary is machine-generated.

A new criterion for generalized estimating equations (GEE) improves working correlation structure selection. This method, using a bias-corrected sandwich estimator, outperforms existing criteria in simulations for correlated binary data.

Keywords:
Rotnitzky-Jewell criteriabias-corrected sandwich covariance estimatorcorrelation information criterionmodel-based covariance estimator

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Generalized Estimating Equations (GEE) rely on a working correlation matrix to model dependencies in repeated measures.
  • Accurate specification of the working correlation structure is crucial for efficient regression coefficient estimation in GEE.
  • Existing criteria like Rotnitzky-Jewell, QIC, and CIC assess correlation structure by comparing model-based and sandwich covariance estimators.

Purpose of the Study:

  • To propose a novel criterion for selecting the working correlation structure in GEE.
  • To address the downward bias and variability issues associated with the sandwich covariance estimator used in current criteria.
  • To evaluate the performance of the new criterion against existing methods.

Main Methods:

  • A new selection criterion for GEE working correlation structures was developed, utilizing a bias-corrected sandwich covariance estimator.
  • Simulation studies were conducted using correlated binary responses to compare the proposed criterion with Rotnitzky-Jewell, QIC, and CIC.
  • The performance was evaluated based on the ability to select the correct working correlation structure.

Main Results:

  • The proposed criterion, based on the bias-corrected sandwich estimator, demonstrated superior performance compared to existing methods in simulation studies.
  • The new method generally achieved better accuracy in selecting the appropriate working correlation structure for GEE.
  • An illustrative example using data from the Madras Schizophrenia Study showcased the practical application of the proposed criterion.

Conclusions:

  • The proposed bias-corrected sandwich covariance-based criterion offers an improved approach for selecting working correlation structures in GEE.
  • This new method provides a more reliable tool for researchers analyzing correlated data, particularly binary outcomes.
  • The findings suggest that accounting for bias in covariance estimation leads to more robust model selection in GEE.