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Improving efficiency in cluster-randomized study design and implementation: taking advantage of a crossover

Authors Reich NG, Milstone AM

Received 29 October 2013

Accepted for publication 19 November 2013

Published 19 December 2013 Volume 2014:6 Pages 11—15

DOI https://doi.org/10.2147/OAJCT.S56730

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

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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