Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies
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 b...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/169241 |
| _version_ | 1811680972230361088 |
|---|---|
| author | Yamamoto, Nao Ejima, Keisuke Zoh, Roger S. Brown, Andrew W. |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) Yamamoto, Nao Ejima, Keisuke Zoh, Roger S. Brown, Andrew W. |
| author_sort | Yamamoto, Nao |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:33:32Z |
| format | Journal Article |
| id | ntu-10356/169241 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:33:32Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1692412023-07-16T15:37:44Z Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies Yamamoto, Nao Ejima, Keisuke Zoh, Roger S. Brown, Andrew W. Lee Kong Chian School of Medicine (LKCMedicine) Social sciences::Sociology Biomarkers Diet 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. Published version Japan Society for the Promotion of Science KAKENHI grant 18K18146 and Ejima. Meiji Yasuda Life Foundation of Health and Welfare to Keisuke Ejima. 2023-07-10T04:44:36Z 2023-07-10T04:44:36Z 2023 Journal Article Yamamoto, N., Ejima, K., Zoh, R. S. & Brown, A. W. (2023). Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies. ELife, 12. https://dx.doi.org/10.7554/eLife.83616 2050-084X https://hdl.handle.net/10356/169241 10.7554/eLife.83616 37017635 2-s2.0-85151787196 12 en eLife © 2023 Yamamoto, Ejima et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. application/pdf |
| spellingShingle | Social sciences::Sociology Biomarkers Diet Yamamoto, Nao Ejima, Keisuke Zoh, Roger S. Brown, Andrew W. Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies |
| title | Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies |
| title_full | Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies |
| title_fullStr | Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies |
| title_full_unstemmed | Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies |
| title_short | Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies |
| title_sort | bias in nutrition health associations is not eliminated by excluding extreme reporters in empirical or simulation studies |
| topic | Social sciences::Sociology Biomarkers Diet |
| url | https://hdl.handle.net/10356/169241 |
| work_keys_str_mv | AT yamamotonao biasinnutritionhealthassociationsisnoteliminatedbyexcludingextremereportersinempiricalorsimulationstudies AT ejimakeisuke biasinnutritionhealthassociationsisnoteliminatedbyexcludingextremereportersinempiricalorsimulationstudies AT zohrogers biasinnutritionhealthassociationsisnoteliminatedbyexcludingextremereportersinempiricalorsimulationstudies AT brownandreww biasinnutritionhealthassociationsisnoteliminatedbyexcludingextremereportersinempiricalorsimulationstudies |