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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...

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Autores principales: Perrier, Flavie, Giorgis-Allemand, Lise, Slama, Rémy, Philippat, Claire
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2016
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.
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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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