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Related Experiment Videos

Estimating equations for association structures.

Jun Yan1, Jason Fine

  • 1Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.

Statistics in Medicine
|March 18, 2004
PubMed
Summary
This summary is machine-generated.

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This study validates robust variance estimators for generalized estimating equations in family studies. Findings show sandwich and jackknife methods accurately estimate correlation parameters, contradicting prior software limitations.

Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Longitudinal Data Analysis

Background:

  • Generalized estimating equations (GEE) are crucial for analyzing correlated data, particularly in family and genetic studies.
  • Accurate estimation of association parameters and their variances is essential for reliable inference.

Purpose of the Study:

  • To investigate and validate robust variance estimators for association parameters within GEE frameworks.
  • To compare the performance of 'sandwich' and jackknife variance estimators for correlation parameters.
  • To address discrepancies with previous findings regarding variance estimation software (MAREG).

Main Methods:

  • Utilized generalized estimating equations with separate link functions for mean, scale, and correlation parameters.
  • Conducted simulation studies with 50 clusters to assess variance estimator performance.

Related Experiment Videos

  • Developed and tested a general jackknife variance estimator formula.

    • Main Results:

    • The 'sandwich' and jackknife variance estimators closely approximated empirical variances for correlation parameters in simulations.
    • Identified limitations in the MAREG software's 'sandwich' estimator, attributing it to unaddressed scale parameter variability.
    • Demonstrated a deficiency in a previously proposed approximate jackknife formula, while the new general formula performed well.

    Conclusions:

    • The proposed robust variance estimators are reliable for association parameter estimation in GEE.
    • Accurate variance estimation is critical for biomedical applications, as illustrated by the alcoholism genetics data.
    • The findings provide improved methods for covariance estimation in complex family studies.