Unaccounted for regression to the mean renders conclusion of article titled “Uric acid lowering in relation to HbA1c reductions with the SGLT2 inhibitor tofogliflozin” unsubstantiated

Kahathuduwa, Chanaka N.; Thomas, Diana M.; Siu, Cynthia; Allison, David B.

doi:10.1111/dom.13323

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…We thank Dr Chanaka N. Kahathuduwa and colleagues for their thoughtful comments on our recent article which appeared in Diabetes, Obesity and Metabolism . We completely understand the points made by Kahathuduwa et al Certainly, if the authors' conclusion is based only on the impact of regression to the mean, RTM might generally be insufficient.…”

Section: Baseline Factors Influencing Degrees Of Uric Acid Reductionmentioning

confidence: 94%

This is in reply to the Letter by Kahathuduwa et al. titled “Unaccounted for regression to the mean renders conclusion of article titled ‘Uric acid lowering in relation to HbA1c reductions with the SGLT2 inhibitor Tofogliflozin’ unsubstantiated”

Ouchi

Oba²,

Suganami

et al. 2018

Diabetes Obesity Metabolism

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Section: Baseline Factors Influencing Degrees Of Uric Acid Reductionmentioning

confidence: 94%

This is in reply to the Letter by Kahathuduwa et al. titled “Unaccounted for regression to the mean renders conclusion of article titled ‘Uric acid lowering in relation to HbA1c reductions with the SGLT2 inhibitor Tofogliflozin’ unsubstantiated”

Ouchi

Oba²,

Suganami

et al. 2018

Diabetes Obesity Metabolism

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Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research

Thomas

Clark

Turner

et al. 2020

The American Journal of Clinical Nutrition

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Background 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). Objectives To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research. Methods We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM. Results Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect. Conclusions The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.

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Regression to the Mean: A Statistical Phenomenon of Worthy Consideration in Anemia Research

Cochrane

Williams

Fischer

et al. 2020

Current Developments in Nutrition

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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 < 120g/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 To calculate and apply RTM to a previously conducted iron supplementation trial of women in Cambodia at increasingly severe baseline anemia cut-offs (hemoglobin < 120g/L, <115g/L and <110g/L). Methods Women received either 60mg/day iron (n = 191) or placebo (n = 185) for 12 weeks. Hemoglobin was measured at baseline and 12 weeks (endline), and change in hemoglobin was calculated in each group, for each cut-off. RTM was calculated in the placebo group at each cut-off and applied to the change observed at each cut-off in the iron group to obtain the RTM-free effect. Results In the placebo group, mean change in hemoglobin increased as cut-offs became more extreme (0.9g/L to 1.9g/L in those with baseline hemoglobin < 120g/L and <110g/L, respectively). RTM estimates similarly increased: 1.0g/L (<120g/L), 1.3g/L (<115g/L), and 1.8g/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 cut-off. 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 cut-off: 8.7g/L (<120g/L), 10.9g/L (<115g/L), and 13.6g/L (<110g/L). Conclusion 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.

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Unaccounted for regression to the mean renders conclusion of article titled “Uric acid lowering in relation to HbA1c reductions with the SGLT2 inhibitor tofogliflozin” unsubstantiated

Cited by 4 publications

References 15 publications

This is in reply to the Letter by Kahathuduwa et al. titled “Unaccounted for regression to the mean renders conclusion of article titled ‘Uric acid lowering in relation to HbA1c reductions with the SGLT2 inhibitor Tofogliflozin’ unsubstantiated”

This is in reply to the Letter by Kahathuduwa et al. titled “Unaccounted for regression to the mean renders conclusion of article titled ‘Uric acid lowering in relation to HbA1c reductions with the SGLT2 inhibitor Tofogliflozin’ unsubstantiated”

Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research

Regression to the Mean: A Statistical Phenomenon of Worthy Consideration in Anemia Research

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