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Performance of generalized estimating equations in practical situations

S R Lipsitz1, G M Fitzmaurice, E J Orav

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.

Biometrics
|March 1, 1994
PubMed
Summary
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Generalized estimating equations (GEE) offer efficient analysis for repeated binary data. One-step GEE estimators perform similarly to fully iterated ones, especially in small samples, avoiding convergence issues.

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Generalized estimating equations (GEE) are established methods for analyzing repeated binary responses.
  • Existing GEE approaches by Liang and Zeger (1986) and Prentice (1988) estimate parameters for expected values and response correlations.

Purpose of the Study:

  • To evaluate the performance of one-step generalized estimating equation (GEE) estimators compared to fully iterated GEE estimators in small sample sizes.
  • To assess the efficiency of GEE methods against ordinary logistic regression with variance correction for time-varying covariates.

Main Methods:

  • Simulation studies were conducted to compare the performance of one-step and fully iterated GEE estimators.
  • Efficiency comparisons were made between GEE methods and ordinary logistic regression with variance correction.

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Main Results:

  • The performance of one-step GEE estimators was found to be qualitatively similar to fully iterated GEE estimators in simulations.
  • GEE methods demonstrated higher efficiency than ordinary logistic regression with variance correction for estimating time-varying covariate effects.

Conclusions:

  • One-step GEE estimators are recommended for small sample sizes with high association between binary responses to avoid convergence problems.
  • GEE methods provide a more efficient approach for analyzing repeated binary data, particularly concerning time-varying covariates.