title: Meta-analysis of community-based cluster randomization trials with binary outcomes.
abstract: BACKGROUND
  Cluster randomization trials are widely used to test the effect of an intervention when individuals are naturally found in groups such as communities. For several separate studies of a similar intervention, it may be of interest to combine their results using meta-analysis procedures. However, this task requires consideration of both the likely dependencies among cluster members (intracluster correlation) and stratification based on the studies considered.
  
  
  OBJECTIVE
  In this article, several possible approaches for meta-analysis are considered for cluster randomization trials having a binary outcome.
  
  
  METHODS
  It is first noted that the standard Mantel-Haenszel test is invalid in this context since it ignores dependencies among cluster members. Two modifications are therefore considered as well as a general inverse variance approach and a procedure based on the Woolf statistic which does not require the availability of trial-specific design effects. Empirical Type I errors and powers for the different procedures considered are evaluated using Monte Carlo simulation. To illustrate the techniques, data are used from trials performed in four countries to compare two antenatal care programs with respect to their effects on the risk of hypertension during pregnancy.
  
  
  RESULTS
  For the simulation scenarios considered, an adjusted Mantel-Haenszel procedure provides a valid test with the greatest power slightly outperforming the general inverse variance approach.
  
  
  CONCLUSIONS
  The potential need to adjust for possible confounding was not considered. However, more detailed information on confounders would not likely be available for most meta-analyses.
  
  
  CONCLUSIONS
  Two procedures performed well. However, the choice of analysis approach also inevitably depends on the nature and extent of the available data.
authors.affiliation: Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada. gdarling@uoguelph.ca
authors.forename: Gerarda A
authors.initials: GA
authors.lastname: Darlington
authors.forename: Allan
authors.initials: A
authors.lastname: Donner
mesh_terms: D002170:Canada; D016000:Cluster Analysis; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D015201:Meta-Analysis as Topic; D016032:Randomized Controlled Trials as Topic