Analysing cluster randomised controlled trials using MLE, GEE, GEE2 and QIF: results from four case studies

Abstract

Abstract Background: Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. Methods: Four modelling approaches (Generalized Linear Mixed Models with parameters/coefficients estimated by Maximum likelihood; Generalized Linear Models with parameters/coefficients estimated by Generalized Estimating Equations (1st order or second order) or Quadratic Inference Function) for the analysis of correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. These four methods are applied to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100 and individual participants ranging from 748 to 9,207. Results are obtained for both continuous and binary outcomes using the statistical packages, R and SAS. Results: The intracluster correlation coefficient (ICC) for each of the case studies was small (<0.05) indicating little dependence of the outcomes related to cluster allocation. In most cases the four methods produced similar results. However, in a few analyses quadratic inference function produced different results compared to the other three methods. Conclusion: This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, a plausible reason could be the negligible correlation (small ICCs) observed among responses in the four case studies. Due to the small ICC values obtained the generalisability of our results is limited. It is important to conduct simulation studies to comprehensively investigate the performance of the four modelling approaches.