Analysis of Individual Data Adjusting for the Clustering Effect
REGRESSION
We estimated logistic regression models for binary outcomes using generalized estimating equations with an exchangeable correlation structure to adjust for clustering effects. We present an unadjusted analysis comparing the intervention and control group at the endpoint, controlling only for clustering. Covariates were not added to the model, as the intervention and control arm did not differ on any baseline demographic characteristic. The standard errors of parameters in the generalized estimating equations were estimated using the bootstrap method (n=1,000) to account for the small number of clusters (n<50).Linear mixed models were estimated for continuous outcomes using generalized estimating equations with an exchangeable correlation structure to adjust for clustering effects and the bootstrap method (n=1,000) for standard errors to account for the small number of clusters. The beta coefficient is the standardized coefficient showing the effect size of treatment on the outcomes.