Does Exclusion of Extreme Reporters of Energy Intake Make Results Less Biased in Nutrition Studies?
Principal Investigator
Name
Keisuke Ejima
Degrees
Ph.D.
Institution
Indiana University
Position Title
Post-doctoral fellow
Email
About this CDAS Project
Study
IDATA
(Learn more about this study)
Project ID
IDATA-17
Initial CDAS Request Approval
Oct 31, 2017
Title
Does Exclusion of Extreme Reporters of Energy Intake Make Results Less Biased in Nutrition Studies?
Summary
Energy intake (EI) is a key factor of energy balance, as is energy expenditure, and known to be associated with obesity and is plausibly related to other health outcomes. Therefore, study of EI is of vital interest in nutrition/obesity studies. Self-report energy intake (SREI) derived from food diaries and other methods is frequently used in those study fields, however, it is well known that SREI is severely error-prone and usually underestimates (sometimes overestimates) actual energy intake. As an accurate measurement of EI, doubly labeled water (DLW) method is widely accepted.
To address the issue of SREI, in many nutrition/obesity studies, outliers (reporting extremely high or low EI) are excluded from analyses based on ‘Goldberg’s rule’ (Goldberg et al., EJCN 1991), which is believed to make results from analyses less biased. However, whether use of this procedure actually makes results less biased has not been tested in empirical studies and we know of no formal theoretical foundation (i.e., mathematical proof) for claiming that it does so.
In this study, we examine whether applying Goldberg’s rule to a dataset make results less biased in nutrition/obesity studies through a test case study using the NCI iData study dataset. In this study, three linear regression analyses will be conducted for each of several health measurements (e.g., weight, blood pressure and blood cholesterol level) as outcomes. The first and second analyses will both use SREI as a main predictor. However, implausible SREI measurements will be excluded from the second analysis using Goldberg’s rule. The third analysis will use EI estimated from DLW, which is a ‘gold standard’, as a main predictor. The estimated coefficients from each analysis are compared to evaluate whether applying Goldberg’s rule to SREI makes the estimated association between EI and the health outcomes less biased. We will also address the research question through a complementary set of computer simulation studies in which data are generated to emulate the distributions of the real observed data.
To address the issue of SREI, in many nutrition/obesity studies, outliers (reporting extremely high or low EI) are excluded from analyses based on ‘Goldberg’s rule’ (Goldberg et al., EJCN 1991), which is believed to make results from analyses less biased. However, whether use of this procedure actually makes results less biased has not been tested in empirical studies and we know of no formal theoretical foundation (i.e., mathematical proof) for claiming that it does so.
In this study, we examine whether applying Goldberg’s rule to a dataset make results less biased in nutrition/obesity studies through a test case study using the NCI iData study dataset. In this study, three linear regression analyses will be conducted for each of several health measurements (e.g., weight, blood pressure and blood cholesterol level) as outcomes. The first and second analyses will both use SREI as a main predictor. However, implausible SREI measurements will be excluded from the second analysis using Goldberg’s rule. The third analysis will use EI estimated from DLW, which is a ‘gold standard’, as a main predictor. The estimated coefficients from each analysis are compared to evaluate whether applying Goldberg’s rule to SREI makes the estimated association between EI and the health outcomes less biased. We will also address the research question through a complementary set of computer simulation studies in which data are generated to emulate the distributions of the real observed data.
Aims
Examine whether applying Goldberg’s rule to SREI reliably reduces bias in parameter estimates in nutrition/obesity studies.
Collaborators
David B. Allison, Indiana University-Bloomington
Steven Heymsfield, Pennington Biomedical Research Center
Dale A. Schoeller, University of Wisconsin - Madison
Related Publications
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Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies.
Yamamoto N, Ejima K, Zoh RS, Brown AW
Elife. 2023 Apr 5; Volume 12 PUBMED -
Does exclusion of extreme reporters of energy intake (the "Goldberg cutoffs") reliably reduce or eliminate bias in nutrition studies? Analysis with illustrative associations of energy intake with health outcomes.
Ejima K, Brown AW, Schoeller DA, Heymsfield SB, Nelson EJ, Allison DB
Am. J. Clin. Nutr. 2019 Nov 1; Volume 110 (Issue 5): Pages 1231-1239 PUBMED