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A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data
BACKGROUND: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, h...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5064985/ https://www.ncbi.nlm.nih.gov/pubmed/27737637 http://dx.doi.org/10.1186/s12874-016-0240-1 |
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author | Agogo, George O. van der Voet, Hilko van ’t Veer, Pieter Ferrari, Pietro Muller, David C. Sánchez-Cantalejo, Emilio Bamia, Christina Braaten, Tonje Knüppel, Sven Johansson, Ingegerd van Eeuwijk, Fred A. Boshuizen, Hendriek C. |
author_facet | Agogo, George O. van der Voet, Hilko van ’t Veer, Pieter Ferrari, Pietro Muller, David C. Sánchez-Cantalejo, Emilio Bamia, Christina Braaten, Tonje Knüppel, Sven Johansson, Ingegerd van Eeuwijk, Fred A. Boshuizen, Hendriek C. |
author_sort | Agogo, George O. |
collection | PubMed |
description | BACKGROUND: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data. METHODS: We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study. RESULTS: Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations. CONCLUSIONS: The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-016-0240-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5064985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50649852016-10-18 A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data Agogo, George O. van der Voet, Hilko van ’t Veer, Pieter Ferrari, Pietro Muller, David C. Sánchez-Cantalejo, Emilio Bamia, Christina Braaten, Tonje Knüppel, Sven Johansson, Ingegerd van Eeuwijk, Fred A. Boshuizen, Hendriek C. BMC Med Res Methodol Research Article BACKGROUND: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data. METHODS: We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study. RESULTS: Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations. CONCLUSIONS: The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-016-0240-1) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-13 /pmc/articles/PMC5064985/ /pubmed/27737637 http://dx.doi.org/10.1186/s12874-016-0240-1 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Agogo, George O. van der Voet, Hilko van ’t Veer, Pieter Ferrari, Pietro Muller, David C. Sánchez-Cantalejo, Emilio Bamia, Christina Braaten, Tonje Knüppel, Sven Johansson, Ingegerd van Eeuwijk, Fred A. Boshuizen, Hendriek C. A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data |
title | A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data |
title_full | A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data |
title_fullStr | A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data |
title_full_unstemmed | A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data |
title_short | A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data |
title_sort | method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5064985/ https://www.ncbi.nlm.nih.gov/pubmed/27737637 http://dx.doi.org/10.1186/s12874-016-0240-1 |
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