Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research
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Authors
Thomas, Diana M.
Clark, Nicholas J.
Turner, Dusty
Siu, Cynthia
Halliday, Tanya M.
Hannon, Bridget A.
Kahathuduwa, Chanaka N.
Kroeger, Cynthia M.
Zoh, Roger
Allison, David B.
Issue Date
2020
Type
journal-article
Language
Keywords
Nutrition and obesity research , Regression to the mean , Statistical errors , Treatment effect , Unsupported conclusions
Alternative Title
Abstract
Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution).
Description
Citation
Thomas DM, Clark N, Turner D, Siu C, Halliday TM, Hannon BA, Kahathuduwa CN, Kroeger CM, Zoh R, Allison DB. Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research. Am J Clin Nutr. 2020 Feb 1;111(2):256-265. doi: 10.1093/ajcn/nqz196. PMID: 31552422; PMCID: PMC6997628.
Publisher
The American Journal of Clinical Nutrition
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Journal
Volume
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PubMed ID
ISSN
0002-9165