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Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology

In 1965, Sir Austin Bradford Hill published nine “viewpoints” to help determine if observed epidemiologic associations are causal. Since then, the “Bradford Hill Criteria” have become the most frequently cited framework for causal inference in epidemiologic studies. However, when Hill published his...

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Autores principales: Fedak, Kristen M., Bernal, Autumn, Capshaw, Zachary A., Gross, Sherilyn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4589117/
https://www.ncbi.nlm.nih.gov/pubmed/26425136
http://dx.doi.org/10.1186/s12982-015-0037-4
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author Fedak, Kristen M.
Bernal, Autumn
Capshaw, Zachary A.
Gross, Sherilyn
author_facet Fedak, Kristen M.
Bernal, Autumn
Capshaw, Zachary A.
Gross, Sherilyn
author_sort Fedak, Kristen M.
collection PubMed
description In 1965, Sir Austin Bradford Hill published nine “viewpoints” to help determine if observed epidemiologic associations are causal. Since then, the “Bradford Hill Criteria” have become the most frequently cited framework for causal inference in epidemiologic studies. However, when Hill published his causal guidelines—just 12 years after the double-helix model for DNA was first suggested and 25 years before the Human Genome Project began—disease causation was understood on a more elementary level than it is today. Advancements in genetics, molecular biology, toxicology, exposure science, and statistics have increased our analytical capabilities for exploring potential cause-and-effect relationships, and have resulted in a greater understanding of the complexity behind human disease onset and progression. These additional tools for causal inference necessitate a re-evaluation of how each Bradford Hill criterion should be interpreted when considering a variety of data types beyond classic epidemiology studies. Herein, we explore the implications of data integration on the interpretation and application of the criteria. Using examples of recently discovered exposure–response associations in human disease, we discuss novel ways by which researchers can apply and interpret the Bradford Hill criteria when considering data gathered using modern molecular techniques, such as epigenetics, biomarkers, mechanistic toxicology, and genotoxicology.
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spelling pubmed-45891172015-10-01 Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology Fedak, Kristen M. Bernal, Autumn Capshaw, Zachary A. Gross, Sherilyn Emerg Themes Epidemiol Analytic Perspective In 1965, Sir Austin Bradford Hill published nine “viewpoints” to help determine if observed epidemiologic associations are causal. Since then, the “Bradford Hill Criteria” have become the most frequently cited framework for causal inference in epidemiologic studies. However, when Hill published his causal guidelines—just 12 years after the double-helix model for DNA was first suggested and 25 years before the Human Genome Project began—disease causation was understood on a more elementary level than it is today. Advancements in genetics, molecular biology, toxicology, exposure science, and statistics have increased our analytical capabilities for exploring potential cause-and-effect relationships, and have resulted in a greater understanding of the complexity behind human disease onset and progression. These additional tools for causal inference necessitate a re-evaluation of how each Bradford Hill criterion should be interpreted when considering a variety of data types beyond classic epidemiology studies. Herein, we explore the implications of data integration on the interpretation and application of the criteria. Using examples of recently discovered exposure–response associations in human disease, we discuss novel ways by which researchers can apply and interpret the Bradford Hill criteria when considering data gathered using modern molecular techniques, such as epigenetics, biomarkers, mechanistic toxicology, and genotoxicology. BioMed Central 2015-09-30 /pmc/articles/PMC4589117/ /pubmed/26425136 http://dx.doi.org/10.1186/s12982-015-0037-4 Text en © Fedak et al. 2015 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 Analytic Perspective
Fedak, Kristen M.
Bernal, Autumn
Capshaw, Zachary A.
Gross, Sherilyn
Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology
title Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology
title_full Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology
title_fullStr Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology
title_full_unstemmed Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology
title_short Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology
title_sort applying the bradford hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology
topic Analytic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4589117/
https://www.ncbi.nlm.nih.gov/pubmed/26425136
http://dx.doi.org/10.1186/s12982-015-0037-4
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