BACKGROUND: Regression to the mean (RTM) is a statistical phenomenon where second measurements are more likely to be closer to the mean. This is particularly observed in those with baseline values further from the mean. Anemic individuals (hemoglobin <120 g/L) are often recruited when evaluating iron supplementation programs, as they are more likely to elicit a greater hemoglobin response; however, they are also at greater risk for RTM as their baseline values are lower than the overall population mean. OBJECTIVE: The aim was to calculate and apply RTM to a previously conducted iron supplementation trial of women in Cambodia at increasingly severe baseline anemia cutoffs (hemoglobin <120 g/L, <115 g/L, and <110 g/L). METHODS: Women received either 60 mg/d iron (n = 191) or placebo (n = 185) for 12 wk. Hemoglobin was measured at baseline and at 12 wk (endline), and change in hemoglobin was calculated in each group for each cutoff. RTM was calculated in the placebo group at each cutoff and applied to the change observed at each cutoff in the iron group to obtain the RTM-free effect. RESULTS: In the placebo group, mean change in hemoglobin increased as cutoffs became more extreme (0.9 g/L to 1.9 g/L in those with baseline hemoglobin <120 g/L and <110 g/L, respectively). RTM estimates similarly increased: 1.0 g/L (<120 g/L), 1.3 g/L (<115 g/L), and 1.8 g/L (<110g/L). When applying RTM to the iron group, we found that ∼10% of the "treatment effect" could be attributable to RTM at each cutoff. However, iron supplementation was still effective in increasing hemoglobin, with an increased effect in those with lower baseline values, as proven by the RTM-free effect at each cutoff: 8.7 g/L (<120 g/L), 10.9 g/L (<115 g/L), and 13.6g/L (<110 g/L). CONCLUSIONS: RTM may have accounted for ∼10% of the observed change in hemoglobin following iron supplementation; however, appropriate use of a placebo group in the statistical analyses of the trial controls for this potential RTM effect.
BACKGROUND: Regression to the mean (RTM) is a statistical phenomenon where second measurements are more likely to be closer to the mean. This is particularly observed in those with baseline values further from the mean. Anemic individuals (hemoglobin <120 g/L) are often recruited when evaluating iron supplementation programs, as they are more likely to elicit a greater hemoglobin response; however, they are also at greater risk for RTM as their baseline values are lower than the overall population mean. OBJECTIVE: The aim was to calculate and apply RTM to a previously conducted iron supplementation trial of women in Cambodia at increasingly severe baseline anemia cutoffs (hemoglobin <120 g/L, <115 g/L, and <110 g/L). METHODS: Women received either 60 mg/d iron (n = 191) or placebo (n = 185) for 12 wk. Hemoglobin was measured at baseline and at 12 wk (endline), and change in hemoglobin was calculated in each group for each cutoff. RTM was calculated in the placebo group at each cutoff and applied to the change observed at each cutoff in the iron group to obtain the RTM-free effect. RESULTS: In the placebo group, mean change in hemoglobin increased as cutoffs became more extreme (0.9 g/L to 1.9 g/L in those with baseline hemoglobin <120 g/L and <110 g/L, respectively). RTM estimates similarly increased: 1.0 g/L (<120 g/L), 1.3 g/L (<115 g/L), and 1.8 g/L (<110g/L). When applying RTM to the iron group, we found that ∼10% of the "treatment effect" could be attributable to RTM at each cutoff. However, iron supplementation was still effective in increasing hemoglobin, with an increased effect in those with lower baseline values, as proven by the RTM-free effect at each cutoff: 8.7 g/L (<120 g/L), 10.9 g/L (<115 g/L), and 13.6g/L (<110 g/L). CONCLUSIONS: RTM may have accounted for ∼10% of the observed change in hemoglobin following iron supplementation; however, appropriate use of a placebo group in the statistical analyses of the trial controls for this potential RTM effect.
Authors: Diana M Thomas; Nicholas Clark; Dusty Turner; Cynthia Siu; Tanya M Halliday; Bridget A Hannon; Chanaka N Kahathuduwa; Cynthia M Kroeger; Roger Zoh; David B Allison Journal: Am J Clin Nutr Date: 2020-02-01 Impact factor: 7.045
Authors: S R Cummings; L Palermo; W Browner; R Marcus; R Wallace; J Pearson; T Blackwell; S Eckert; D Black Journal: JAMA Date: 2000-03-08 Impact factor: 56.272
Authors: Lulu X Pei; Hou Kroeun; Suzanne M Vercauteren; Susan I Barr; Tim J Green; Arianne Y Albert; Crystal D Karakochuk Journal: J Nutr Date: 2021-08-07 Impact factor: 4.798