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

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dc.citation.issue6
dc.citation.volume7
dc.contributor.authorCorbin M
dc.contributor.authorRichiardi L
dc.contributor.authorVermeulen R
dc.contributor.authorKromhout H
dc.contributor.authorMerletti F
dc.contributor.authorPeters S
dc.contributor.authorSimonato L
dc.contributor.authorSteenland K
dc.contributor.authorPearce NE
dc.contributor.authorMaule M
dc.date.available2012-06-11
dc.date.available2012-05-14
dc.date.issued11/06/2012
dc.description.abstractBackground 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. Methodology/Principal Findings 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). Conclusions/Significance 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.
dc.description.publication-statusPublished
dc.format.extent? - ? (8)
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000305337600059&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifierARTN e38944
dc.identifier.citationPLOS ONE, 2012, 7 (6), pp. ? - ? (8)
dc.identifier.doi10.1371/journal.pone.0038944
dc.identifier.elements-id220677
dc.identifier.harvestedMassey_Dark
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/10179/11893
dc.languageEnglish
dc.publisherPublic Library of Science
dc.relation.isPartOfPLOS ONE
dc.subjectScience & Technology
dc.subjectMultidisciplinary Sciences
dc.subjectScience & Technology - Other Topics
dc.subjectMULTIDISCIPLINARY SCIENCES
dc.subjectSEMI-BAYES
dc.subjectEMPIRICAL-BAYES
dc.subjectEXPOSURES
dc.subjectSURVEILLANCE
dc.titleHeirarchical regression for multiple comparisons in a case-control study of occupational risks for lung cancer.
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Health
pubs.organisational-group/Massey University/College of Health/Research Centre for Hauora and Health
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