Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies

eLife · 5 authors, 9 centres

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TL;DR

This empirical and simulation study evaluated whether applying Goldberg cutoffs to exclude extreme reporters reduces bias in the associations between self-reported nutrition intakes and health outcomes. The central result is that while Goldberg cutoffs reduce the bias, they do not completely eliminate it.

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The study used empirical data from the IDATA dataset and computer simulations to assess reporting bias in self-reported nutrition intakes (energy, sodium, potassium, protein) compared to biomarker-based measurements. Methods included calculating percent bias in linear regression coefficients and percent remaining bias after applying Goldberg cutoffs to exclude extreme reporters. Central results showed that bias in mean nutrition intake was eliminated by the Goldberg rule, but bias in health outcome associations (specifically for energy intake with body weight and waist circumference) was reduced but not removed. Limitations include the use of bivariate analyses, assumptions in simulation modeling, and a predominantly white, older study population. The implication is that excluding extreme reporters is more effective for estimating mean nutrition intake than for studying associations between nutrition and health.

STUDY TYPE

other

SPECIALTY

nutrition

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AI pipeline

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100% · A

ORIGINAL PAPER

PMC10076015

Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies

eLife

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ABSTRACT

Abstract Self-reported nutrition intake (NI) data are prone to reporting bias that may induce bias in estimands in nutrition studies; however, they are used anyway due to high feasibility. We examined whether applying Goldberg cutoffs to remove ‘implausible’ self-reported NI could reliably reduce bias compared to biomarkers for energy, sodium, potassium, and protein. Using the Interactive Diet and Activity Tracking in the American Association of Retired Persons (IDATA) data, significant bias in mean NI was removed with Goldberg cutoffs (120 among 303 participants excluded). Associations between NI and health outcomes (weight, waist circumference, heart rate, systolic/diastolic blood pressure, and VO2 max) were estimated, but sample size was insufficient to evaluate bias reductions. We therefore simulated data based on IDATA. Significant bias in simulated associations using self-reported NI was reduced but not completely eliminated by Goldberg cutoffs in 14 of 24 nutrition-outcome pairs; bias was not reduced for the remaining 10 cases. Also, 95% coverage probabilities were improved by applying Goldberg cutoffs in most cases but underperformed compared with biomarker data. Although Goldberg cutoffs may achieve bias elimination in estimating mean NI, bias in estimates of associations between NI and outcomes will not necessarily be reduced or eliminated after application of Goldberg cutoffs. Whether one uses Goldberg cutoffs should therefore be decided based on research purposes and not general rules. Research organism: Human

CITATION EXPORT

Nao Yamamoto, Keisuke Ejima, Roger S Zoh et al.. Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies. eLife. (2023). https://doi.org/10.7554/eLife.83616

AUTHORS

5 · 9 CENTRES

Nao Yamamoto, Keisuke Ejima, Roger S Zoh, Andrew W Brown, Eduardo L Franco

1School of Human Evolution and Social Change, Arizona State University, Tempe, United States
2Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, United States
3Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
4Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, United States
5Arkansas Children’s Research Institute, Little Rock, United States
6McGill University, Canada
7McGill University, Canada
1McGill University, Canada
1McGill University, Canada