Statistical methods for the meta-analysis of cluster randomization trials

Abstract

Cluster randomization trials have become a very attractive research strategy, particularly for the evaluation of health service interventions. The need to conduct meta-analyses of such trials is also becoming more common. However, as with cluster randomization trials in general, such analyses raise special methodologic challenges. In this paper, we discuss and illustrate several statistical approaches that might be applied to a meta-analysis of cluster randomization trials, each of which has a binary endpoint. Statistical methods for constructing inferences for a summary intervention odds ratio include those based on Mantel-Haenszel procedures, the ratio estimator approach, Woolf procedures and generalized estimating equations using robust variance estimation. The advantages and disadvantages of each method are discussed in the context of an example.