The role and challenges of cluster randomised trials for global health

Evaluating whether an intervention works when trialled in groups of individuals can pose complex challenges for clinical research. Cluster randomised controlled trials involve the random allocation of groups or clusters of individuals to receive an intervention, and they are commonly used in global health research. In this paper, we describe the potential reasons for the increasing popularity of cluster trials in low-income and middle-income countries. We also draw on key areas of global health research for an assessment of common trial planning practices, and we address their methodological shortcomings and pitfalls. Lastly, we discuss alternative approaches for population-level intervention trials that could be useful for research undertaken in low-income and middle-income countries for situations in which the use of cluster randomisation might not be appropriate

Handling intra-cluster correlation when analyzing the effects of decision support on health care process measures

The clinical worksite constitutes a naturally clustered environment, posing challenges in the statistical analysis of quality improvement interventions such as computerized decision support. Ignoring clustering in the analysis may lead to biased effect estimates, underestimating the variance and hence type I errors. This paper presents a secondary analysis on data from a previously published, cluster randomized trial in cardiac rehabilitation. We compared six different statistical analysis methods (weighted and unweighted t-test; adjusted χ2 test; normal and multilevel logistic regression analysis; and generalized estimation equations). There were considerable differences in both point estimates and p-values derived by the methods, and differences were larger with increasing intracluster correlatio