Errors in the implementation, analysis, and reporting of randomization within obesity and nutrition research: a guide to their avoidance

Colby J. Vorland, Andrew W. Brown, John A. Dawson, Stephanie L. Dickinson, Lilian Golzarri-Arroyo, Bridget A. Hannon, Moonseong Heo, Steven B. Heymsfield, Wasantha P. Jayawardene, Chanaka N. Kahathuduwa, Scott W. Keith, J. Michael Oakes, Carmen D. Tekwe, Lehana Thabane, David B. Allison

Research output: Contribution to journalReview articlepeer-review

Abstract

Randomization is an important tool used to establish causal inferences in studies designed to further our understanding of questions related to obesity and nutrition. To take advantage of the inferences afforded by randomization, scientific standards must be upheld during the planning, execution, analysis, and reporting of such studies. We discuss ten errors in randomized experiments from real-world examples from the literature and outline best practices for their avoidance. These ten errors include: representing nonrandom allocation as random, failing to adequately conceal allocation, not accounting for changing allocation ratios, replacing subjects in nonrandom ways, failing to account for non-independence, drawing inferences by comparing statistical significance from within-group comparisons instead of between-groups, pooling data and breaking the randomized design, failing to account for missing data, failing to report sufficient information to understand study methods, and failing to frame the causal question as testing the randomized assignment per se. We hope that these examples will aid researchers, reviewers, journal editors, and other readers to endeavor to a high standard of scientific rigor in randomized experiments within obesity and nutrition research.

Original languageEnglish
Pages (from-to)2335-2346
Number of pages12
JournalInternational Journal of Obesity
Volume45
Issue number11
DOIs
StatePublished - Nov 2021

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