Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.

<h4>Background</h4>Occupational studies often involve multiple comparisons and therefore suffer from false positive findings. Semi-Bayes adjustment methods have sometimes been used to address this issue. Hierarchical regression is a more general approach, including Semi-Bayes adjustment...

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Autores principales:	Marine Corbin, Lorenzo Richiardi, Roel Vermeulen, Hans Kromhout, Franco Merletti, Susan Peters, Lorenzo Simonato, Kyle Steenland, Neil Pearce, Milena Maule
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5088ee611e7f48d6aaa7d383b1ef5556
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spelling	oai:doaj.org-article:5088ee611e7f48d6aaa7d383b1ef55562021-11-18T07:15:48ZHierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.1932-620310.1371/journal.pone.0038944https://doaj.org/article/5088ee611e7f48d6aaa7d383b1ef55562012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22701732/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Occupational studies often involve multiple comparisons and therefore suffer from false positive findings. Semi-Bayes adjustment methods have sometimes been used to address this issue. Hierarchical regression is a more general approach, including Semi-Bayes adjustment as a special case, that aims at improving the validity of standard maximum-likelihood estimates in the presence of multiple comparisons by incorporating similarities between the exposures of interest in a second-stage model.<h4>Methodology/principal findings</h4>We re-analysed data from an occupational case-control study of lung cancer, applying hierarchical regression. In the second-stage model, we included the exposure to three known lung carcinogens (asbestos, chromium and silica) for each occupation, under the assumption that occupations entailing similar carcinogenic exposures are associated with similar risks of lung cancer. Hierarchical regression estimates had smaller confidence intervals than maximum-likelihood estimates. The shrinkage toward the null was stronger for extreme, less stable estimates (e.g., "specialised farmers": maximum-likelihood OR: 3.44, 95%CI 0.90-13.17; hierarchical regression OR: 1.53, 95%CI 0.63-3.68). Unlike Semi-Bayes adjustment toward the global mean, hierarchical regression did not shrink all the ORs towards the null (e.g., "Metal smelting, converting and refining furnacemen": maximum-likelihood OR: 1.07, Semi-Bayes OR: 1.06, hierarchical regression OR: 1.26).<h4>Conclusions/significance</h4>Hierarchical regression could be a valuable tool in occupational studies in which disease risk is estimated for a large amount of occupations when we have information available on the key carcinogenic exposures involved in each occupation. With the constant progress in exposure assessment methods in occupational settings and the availability of Job Exposure Matrices, it should become easier to apply this approach.Marine CorbinLorenzo RichiardiRoel VermeulenHans KromhoutFranco MerlettiSusan PetersLorenzo SimonatoKyle SteenlandNeil PearceMilena MaulePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 6, p e38944 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marine Corbin Lorenzo Richiardi Roel Vermeulen Hans Kromhout Franco Merletti Susan Peters Lorenzo Simonato Kyle Steenland Neil Pearce Milena Maule Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
description	<h4>Background</h4>Occupational studies often involve multiple comparisons and therefore suffer from false positive findings. Semi-Bayes adjustment methods have sometimes been used to address this issue. Hierarchical regression is a more general approach, including Semi-Bayes adjustment as a special case, that aims at improving the validity of standard maximum-likelihood estimates in the presence of multiple comparisons by incorporating similarities between the exposures of interest in a second-stage model.<h4>Methodology/principal findings</h4>We re-analysed data from an occupational case-control study of lung cancer, applying hierarchical regression. In the second-stage model, we included the exposure to three known lung carcinogens (asbestos, chromium and silica) for each occupation, under the assumption that occupations entailing similar carcinogenic exposures are associated with similar risks of lung cancer. Hierarchical regression estimates had smaller confidence intervals than maximum-likelihood estimates. The shrinkage toward the null was stronger for extreme, less stable estimates (e.g., "specialised farmers": maximum-likelihood OR: 3.44, 95%CI 0.90-13.17; hierarchical regression OR: 1.53, 95%CI 0.63-3.68). Unlike Semi-Bayes adjustment toward the global mean, hierarchical regression did not shrink all the ORs towards the null (e.g., "Metal smelting, converting and refining furnacemen": maximum-likelihood OR: 1.07, Semi-Bayes OR: 1.06, hierarchical regression OR: 1.26).<h4>Conclusions/significance</h4>Hierarchical regression could be a valuable tool in occupational studies in which disease risk is estimated for a large amount of occupations when we have information available on the key carcinogenic exposures involved in each occupation. With the constant progress in exposure assessment methods in occupational settings and the availability of Job Exposure Matrices, it should become easier to apply this approach.
format	article
author	Marine Corbin Lorenzo Richiardi Roel Vermeulen Hans Kromhout Franco Merletti Susan Peters Lorenzo Simonato Kyle Steenland Neil Pearce Milena Maule
author_facet	Marine Corbin Lorenzo Richiardi Roel Vermeulen Hans Kromhout Franco Merletti Susan Peters Lorenzo Simonato Kyle Steenland Neil Pearce Milena Maule
author_sort	Marine Corbin
title	Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
title_short	Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
title_full	Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
title_fullStr	Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
title_full_unstemmed	Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
title_sort	hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/5088ee611e7f48d6aaa7d383b1ef5556
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Hierarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.

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