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

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 sim

SUBMITTER: Yamamoto N 

PROVIDER: S-EPMC10076015 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature