Publications

Use of robust randomised clinical trial (RCT) evaluation is crucial to determine which interventions would be useful for public health or clinical care.1,2 Certain interventions are delivered at a population level or at a group level, and these interventions can result in changes to group behaviours, leading to large scale population-level effects.3 The cluster RCT design (herein, cluster trial) is a specific trial design that is used to evaluate interventions delivered at a group level.4 In cluster trials, whole groups of structured collections of individuals or health system service delivery platforms, such as facilities, are randomly assigned to receive interventions, and these groups are referred to as clusters. Examples of clusters include communities, health clinics, or schools. In contrast to the individual RCT, in which the group allocation of interventions is determined by randomisation of individual participants, cluster trials randomly assign interventions to a whole cluster of individuals. Interventions themselves can be administered at a cluster level (eg, mosquito egg traps5) or at an individual level (eg, vaccinations6). In cluster trials, outcomes can be measured at a cluster level6 or at an individual level.7

There is an increasing popularity of the use of cluster trials in low-income and middle-income countries (LMICs).8 There have been many successful high-profile trials that have used a cluster trial design, and many of the design features of cluster trials lend themselves well to priority areas of research set in LMICs. Despite the increasing use of this design, there can be methodological and interpretational challenges. Cluster trials are complex due to the interplay between the similarities of individual participants within a cluster and the differences between clusters.9,10 Because outcomes of individuals within the same cluster are correlated, standard methods for design and analysis of individual RCTs do not suffice for cluster trials. Furthermore, because special considerations are required when designing and analysing cluster trials, there have been great efforts towards unifying and improving the standards of the design and analysis interpretation of cluster trials.9,11 However, some evaluations have identified several design and interpretational challenges to cluster trials that often arise due to inadequate planning.12–14

In this third paper of the Series, we first discuss the attributes of cluster trials in the context of global health research, followed by specific challenges that are associated with planning and implementing such attributes in LMICs. We then draw on examples of cluster trials implemented across key global health research areas, such as maternal, newborn, and child health (MNCH), malaria, and water, sanitation, and hygiene (WASH), to assess the current cluster trial planning practices in LMICs. Lastly, we describe examples of high-quality and innovative LMIC-based cluster trials, (panels 1–3) and we outline alternative approaches and supplementary methodologies to cluster designs that can be used to improve their efficiency.