Complex interventions—those that contain several interacting components—to improve clinical outcomes and population health are growing because of the increasing recognition that multilevel approaches are needed to solve complex problems in health systems and care delivery. (1) In maternal and neonatal health, complex interventions are needed to improve quality of care. Yet few studies of these complex interventions are powered to detect true differences in mortality because of the numbers needed for a relatively rare outcome, such as maternal mortality. Although perinatal mortality is more common, large sample sizes are still required. Studies are often powered to examine outcomes or process measures proximal to mortality, such as morbidity or complications that could lead to mortality. The BetterBirth Trial was one of the largest cluster randomized controlled trials (RCTs) to target maternal and perinatal mortality as primary outcomes. Given its scale, this trial has led to important lessons learned that could be applied to other large-scale studies with complex interventions in maternal and newborn health.
Many epidemiology and biostatistics textbooks have described how to theoretically design RCTs to maximize scientific rigor. (2) However, there are few, if any, resources on how to actually implement complex interventions on a large scale, evaluate their impact, and disseminate the results. (3) Balancing an unbiased and robust study design with the reality of implementing a complex intervention in the real world can pose ongoing challenges for research teams. Some generally accepted best practices to minimize common challenges of trial implementation do exist, such as for institutional review board navigation, site selection, and stakeholder engagement. This perspective reflects on the experience of the BetterBirth Trial and provides insights around best practices of important components of trial design and implementation that may be initially overlooked.
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