Bias-corrected covariance estimators are introduced in the context of an estimating equations approach for intracluster correlations among binary outcomes.
Simulation study results show that the bias-corrected covariance estimators perform better than uncorrected sandwich estimators in terms of bias and coverage probabilities.
Additionally, introduction of a matrix-based bias-correction into the estimating equations considerably improves point and interval estimation for the intracluster correlations.
The methods are illustrated using data from a nested cross-sectional cluster trial on reducing underage drinking
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Finite sample adjustments in estimating equations and covariance estimators for intracluster correlations