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Using surrogate biomarkers to improve measurement error models in nutritional epidemiology
Nutritional epidemiology relies largely on self-reported measures of dietary intake, errors in which give biased estimated diet–disease associations. Self-reported measurements come from questionnaires and food records. Unbiased biomarkers are scarce; however, surrogate biomarkers, which are correla...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3824235/ https://www.ncbi.nlm.nih.gov/pubmed/23553407 http://dx.doi.org/10.1002/sim.5803 |
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author | Keogh, Ruth H White, Ian R Rodwell, Sheila A |
author_facet | Keogh, Ruth H White, Ian R Rodwell, Sheila A |
author_sort | Keogh, Ruth H |
collection | PubMed |
description | Nutritional epidemiology relies largely on self-reported measures of dietary intake, errors in which give biased estimated diet–disease associations. Self-reported measurements come from questionnaires and food records. Unbiased biomarkers are scarce; however, surrogate biomarkers, which are correlated with intake but not unbiased, can also be useful. It is important to quantify and correct for the effects of measurement error on diet–disease associations. Challenges arise because there is no gold standard, and errors in self-reported measurements are correlated with true intake and each other. We describe an extended model for error in questionnaire, food record, and surrogate biomarker measurements. The focus is on estimating the degree of bias in estimated diet–disease associations due to measurement error. In particular, we propose using sensitivity analyses to assess the impact of changes in values of model parameters which are usually assumed fixed. The methods are motivated by and applied to measures of fruit and vegetable intake from questionnaires, 7-day diet diaries, and surrogate biomarker (plasma vitamin C) from over 25000 participants in the Norfolk cohort of the European Prospective Investigation into Cancer and Nutrition. Our results show that the estimated effects of error in self-reported measurements are highly sensitive to model assumptions, resulting in anything from a large attenuation to a small amplification in the diet–disease association. Commonly made assumptions could result in a large overcorrection for the effects of measurement error. Increased understanding of relationships between potential surrogate biomarkers and true dietary intake is essential for obtaining good estimates of the effects of measurement error in self-reported measurements on observed diet–disease associations. Copyright © 2013 John Wiley & Sons, Ltd. |
format | Online Article Text |
id | pubmed-3824235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-38242352013-11-14 Using surrogate biomarkers to improve measurement error models in nutritional epidemiology Keogh, Ruth H White, Ian R Rodwell, Sheila A Stat Med Research Articles Nutritional epidemiology relies largely on self-reported measures of dietary intake, errors in which give biased estimated diet–disease associations. Self-reported measurements come from questionnaires and food records. Unbiased biomarkers are scarce; however, surrogate biomarkers, which are correlated with intake but not unbiased, can also be useful. It is important to quantify and correct for the effects of measurement error on diet–disease associations. Challenges arise because there is no gold standard, and errors in self-reported measurements are correlated with true intake and each other. We describe an extended model for error in questionnaire, food record, and surrogate biomarker measurements. The focus is on estimating the degree of bias in estimated diet–disease associations due to measurement error. In particular, we propose using sensitivity analyses to assess the impact of changes in values of model parameters which are usually assumed fixed. The methods are motivated by and applied to measures of fruit and vegetable intake from questionnaires, 7-day diet diaries, and surrogate biomarker (plasma vitamin C) from over 25000 participants in the Norfolk cohort of the European Prospective Investigation into Cancer and Nutrition. Our results show that the estimated effects of error in self-reported measurements are highly sensitive to model assumptions, resulting in anything from a large attenuation to a small amplification in the diet–disease association. Commonly made assumptions could result in a large overcorrection for the effects of measurement error. Increased understanding of relationships between potential surrogate biomarkers and true dietary intake is essential for obtaining good estimates of the effects of measurement error in self-reported measurements on observed diet–disease associations. Copyright © 2013 John Wiley & Sons, Ltd. Blackwell Publishing Ltd 2013-09-30 2013-04-02 /pmc/articles/PMC3824235/ /pubmed/23553407 http://dx.doi.org/10.1002/sim.5803 Text en Copyright © 2013 John Wiley & Sons, Ltd. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
spellingShingle | Research Articles Keogh, Ruth H White, Ian R Rodwell, Sheila A Using surrogate biomarkers to improve measurement error models in nutritional epidemiology |
title | Using surrogate biomarkers to improve measurement error models in nutritional epidemiology |
title_full | Using surrogate biomarkers to improve measurement error models in nutritional epidemiology |
title_fullStr | Using surrogate biomarkers to improve measurement error models in nutritional epidemiology |
title_full_unstemmed | Using surrogate biomarkers to improve measurement error models in nutritional epidemiology |
title_short | Using surrogate biomarkers to improve measurement error models in nutritional epidemiology |
title_sort | using surrogate biomarkers to improve measurement error models in nutritional epidemiology |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3824235/ https://www.ncbi.nlm.nih.gov/pubmed/23553407 http://dx.doi.org/10.1002/sim.5803 |
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