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

Abstract

AbstractSelf-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 IDATA data, significant bias in mean NI was removed with Goldberg cutoffs (120 among 303 participants excluded). Associations between NI and 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 eliminated by Goldberg cutoffs in 14 of 24 nutrition-outcome pairs; bias was not reduced for remaining cases. 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.Impact StatementElimination of extreme reporters using Goldberg cutoffs does not always produce unbiased estimates of associations between nutrition intakes and health outcomes.