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Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies
For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4820663/ https://www.ncbi.nlm.nih.gov/pubmed/27035688 http://dx.doi.org/10.1097/EDE.0000000000000460 |
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author | Perrier, Flavie Giorgis-Allemand, Lise Slama, Rémy Philippat, Claire |
author_facet | Perrier, Flavie Giorgis-Allemand, Lise Slama, Rémy Philippat, Claire |
author_sort | Perrier, Flavie |
collection | PubMed |
description | For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high. METHODS: We considered chemicals with intraclass correlation coefficients of 0.6 and 0.2. In a simulation study, we hypothesized that the chemical urinary concentrations averaged over a given time period were associated with a health outcome and estimated the bias of studies assessing exposure that collected 1 to 50 random biospecimens per subject. We assumed a classical type error. We studied associations using a within-subject pooling approach and two measurement error models (simulation extrapolation and regression calibration), the latter requiring the assay of more than one biospecimen per subject. RESULTS: For both continuous and binary outcomes, using one sample led to attenuation bias of 40% and 80% for compounds with intraclass correlation coefficients of 0.6 and 0.2, respectively. For a compound with an intraclass correlation coefficient of 0.6, the numbers of biospecimens required to limit bias to less than 10% were 6, 2, and 2 biospecimens with the pooling, simulation extrapolation, and regression calibration methods (these values were, respectively, 35, 8, and 2 for a compound with an intraclass correlation coefficient of 0.2). Compared with pooling, these methods did not improve power. CONCLUSION: Within-subject pooling limits attenuation bias without increasing assay costs. Simulation extrapolation and regression calibration further limit bias, compared with the pooling approach, but increase assay costs. |
format | Online Article Text |
id | pubmed-4820663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-48206632016-04-21 Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies Perrier, Flavie Giorgis-Allemand, Lise Slama, Rémy Philippat, Claire Epidemiology Methods For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high. METHODS: We considered chemicals with intraclass correlation coefficients of 0.6 and 0.2. In a simulation study, we hypothesized that the chemical urinary concentrations averaged over a given time period were associated with a health outcome and estimated the bias of studies assessing exposure that collected 1 to 50 random biospecimens per subject. We assumed a classical type error. We studied associations using a within-subject pooling approach and two measurement error models (simulation extrapolation and regression calibration), the latter requiring the assay of more than one biospecimen per subject. RESULTS: For both continuous and binary outcomes, using one sample led to attenuation bias of 40% and 80% for compounds with intraclass correlation coefficients of 0.6 and 0.2, respectively. For a compound with an intraclass correlation coefficient of 0.6, the numbers of biospecimens required to limit bias to less than 10% were 6, 2, and 2 biospecimens with the pooling, simulation extrapolation, and regression calibration methods (these values were, respectively, 35, 8, and 2 for a compound with an intraclass correlation coefficient of 0.2). Compared with pooling, these methods did not improve power. CONCLUSION: Within-subject pooling limits attenuation bias without increasing assay costs. Simulation extrapolation and regression calibration further limit bias, compared with the pooling approach, but increase assay costs. Lippincott Williams & Wilkins 2016-05 2016-04-01 /pmc/articles/PMC4820663/ /pubmed/27035688 http://dx.doi.org/10.1097/EDE.0000000000000460 Text en Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (http://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially. |
spellingShingle | Methods Perrier, Flavie Giorgis-Allemand, Lise Slama, Rémy Philippat, Claire Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies |
title | Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies |
title_full | Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies |
title_fullStr | Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies |
title_full_unstemmed | Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies |
title_short | Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies |
title_sort | within-subject pooling of biological samples to reduce exposure misclassification in biomarker-based studies |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4820663/ https://www.ncbi.nlm.nih.gov/pubmed/27035688 http://dx.doi.org/10.1097/EDE.0000000000000460 |
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