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Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

BACKGROUND: The process of creating a cohort or cohort substudy may induce misleading exposure–health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk. OBJEC...

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Autores principales: Weisskopf, Marc G., Sparrow, David, Hu, Howard, Power, Melinda C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Institute of Environmental Health Sciences 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4629739/
https://www.ncbi.nlm.nih.gov/pubmed/25956004
http://dx.doi.org/10.1289/ehp.1408888
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author Weisskopf, Marc G.
Sparrow, David
Hu, Howard
Power, Melinda C.
author_facet Weisskopf, Marc G.
Sparrow, David
Hu, Howard
Power, Melinda C.
author_sort Weisskopf, Marc G.
collection PubMed
description BACKGROUND: The process of creating a cohort or cohort substudy may induce misleading exposure–health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk. OBJECTIVES: We aimed to demonstrate how such biases of the exposure–health effect association arise and how one may mitigate them. METHODS: We used directed acyclic graphs and the example of bone lead and mortality (all-cause, cardiovascular, and ischemic heart disease) among 835 white men in the Normative Aging Study (NAS) to illustrate potential bias related to recruitment into the NAS and the bone lead substudy. We then applied methods (adjustment, restriction, and inverse probability of attrition weighting) to mitigate these biases in analyses using Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Analyses adjusted for age at bone lead measurement, smoking, and education among all men found HRs (95% CI) for the highest versus lowest tertile of patella lead of 1.34 (0.90, 2.00), 1.46 (0.86, 2.48), and 2.01 (0.86, 4.68) for all-cause, cardiovascular, and ischemic heart disease mortality, respectively. After applying methods to mitigate the biases, the HR (95% CI) among the 637 men analyzed were 1.86 (1.12, 3.09), 2.47 (1.23, 4.96), and 5.20 (1.61, 16.8), respectively. CONCLUSIONS: Careful attention to the underlying structure of the observed data is critical to identifying potential biases and methods to mitigate them. Understanding factors that influence initial study participation and study loss to follow-up is critical. Recruitment of population-based samples and enrolling participants at a younger age, before the potential onset of exposure-related health effects, can help reduce these potential pitfalls. CITATION: Weisskopf MG, Sparrow D, Hu H, Power MC. 2015. Biased exposure–health effect estimates from selection in cohort studies: are environmental studies at particular risk? Environ Health Perspect 123:1113–1122; http://dx.doi.org/10.1289/ehp.1408888
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spelling pubmed-46297392015-11-25 Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk? Weisskopf, Marc G. Sparrow, David Hu, Howard Power, Melinda C. Environ Health Perspect Review BACKGROUND: The process of creating a cohort or cohort substudy may induce misleading exposure–health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk. OBJECTIVES: We aimed to demonstrate how such biases of the exposure–health effect association arise and how one may mitigate them. METHODS: We used directed acyclic graphs and the example of bone lead and mortality (all-cause, cardiovascular, and ischemic heart disease) among 835 white men in the Normative Aging Study (NAS) to illustrate potential bias related to recruitment into the NAS and the bone lead substudy. We then applied methods (adjustment, restriction, and inverse probability of attrition weighting) to mitigate these biases in analyses using Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Analyses adjusted for age at bone lead measurement, smoking, and education among all men found HRs (95% CI) for the highest versus lowest tertile of patella lead of 1.34 (0.90, 2.00), 1.46 (0.86, 2.48), and 2.01 (0.86, 4.68) for all-cause, cardiovascular, and ischemic heart disease mortality, respectively. After applying methods to mitigate the biases, the HR (95% CI) among the 637 men analyzed were 1.86 (1.12, 3.09), 2.47 (1.23, 4.96), and 5.20 (1.61, 16.8), respectively. CONCLUSIONS: Careful attention to the underlying structure of the observed data is critical to identifying potential biases and methods to mitigate them. Understanding factors that influence initial study participation and study loss to follow-up is critical. Recruitment of population-based samples and enrolling participants at a younger age, before the potential onset of exposure-related health effects, can help reduce these potential pitfalls. CITATION: Weisskopf MG, Sparrow D, Hu H, Power MC. 2015. Biased exposure–health effect estimates from selection in cohort studies: are environmental studies at particular risk? Environ Health Perspect 123:1113–1122; http://dx.doi.org/10.1289/ehp.1408888 National Institute of Environmental Health Sciences 2015-05-08 2015-11 /pmc/articles/PMC4629739/ /pubmed/25956004 http://dx.doi.org/10.1289/ehp.1408888 Text en http://creativecommons.org/publicdomain/mark/1.0/ Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, “Reproduced with permission from Environmental Health Perspectives”); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
spellingShingle Review
Weisskopf, Marc G.
Sparrow, David
Hu, Howard
Power, Melinda C.
Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?
title Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?
title_full Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?
title_fullStr Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?
title_full_unstemmed Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?
title_short Biased Exposure–Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?
title_sort biased exposure–health effect estimates from selection in cohort studies: are environmental studies at particular risk?
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4629739/
https://www.ncbi.nlm.nih.gov/pubmed/25956004
http://dx.doi.org/10.1289/ehp.1408888
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