Finite‐Sample Adjustments in Estimating Equations for Correlated Overdispersed Count Outcomes With Application to Cluster Randomized Trials - Ying Zhang; John S. Preisser (Statistics in Medicine, 2026)
ABSTRACT Generalized estimating equations (GEE) produce population‐averaged estimates of treatment effects in marginal mean models for correlated count outcomes, when intra‐cluster correlations are considered as nuisance parameters. For joint inference of marginal means and correlations, paired estimating equations have been proposed. With a small number of clusters, matrix‐adjusted estimating equations (MAEE) were introduced to correct finite‐sample bias in correlation parameter estimates. This article extends the GEE/MAEE method by adding a third estimating equation for the scale parameter (3EE/MAEE) and demonstrates its superior performance for correlated count outcomes. In simulations, 3EE/MAEE reduced bias of ICC estimates and better maintained the nominal coverage of confidence intervals compared to its uncorrected counterpart. The performance of different bias‐corrected sandwich variance estimators as well as working negative binomial (NB) and Poisson models is also evaluated. The 3EE/MAEE method is applied to overdispersed correlated count outcomes from a real‐world, stepped wedge cluster randomized trial.