Improving efficiency in cluster-randomized study design and implementation: taking advantage of a crossover
Nicholas G Reich,1 Aaron M Milstone2,3
1Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, UMass-Amherst, Amherst, MA, USA; 2Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; 3Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
Abstract: While individually randomized trials have long provided the gold standard of clinical evidence, the use of cluster-randomized trials in biomedical and social scientific research has expanded rapidly in recent years. In certain settings, randomizing by group or cluster can provide distinct advantages over individual randomization. However, a central challenge for cluster-randomized trials is ensuring that the study arms are balanced across important participant characteristics. One method to combat imbalance between study arms is to incorporate a crossover into the study design. In this design, every cluster is observed under each treatment condition, in a randomly assigned sequence. We provide a concrete example of how incorporating a crossover into a cluster-randomized study can improve balance between arms and increase statistical efficiency of a trial. However, a crossover design cannot always be effectively implemented. This commentary illustrates the potential benefits and discusses the challenges and disadvantages to incorporating a crossover in a cluster-randomized study design.
Keywords: cluster-randomized clinical trials, crossover design, controlled comparisons, statistical power, balance, efficiency
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