2023-09-28 14:46:10 +00:00
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abstract: 'Healthy worker survivor bias may occur in occupational studies due to
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the tendency for unhealthy individuals to leave work earlier, and
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consequently accrue less exposure, compared with their healthier
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counterparts. If occupational data are not analyzed using appropriate
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methods, this bias can result in attenuation or even reversal of the
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estimated effects of exposures on health outcomes. Recent advances in
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computing power, coupled with state-of-the-art statistical methods, have
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greatly increased the ability of analysts to control healthy worker
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survivor bias. However, these methods have not been widely adopted by
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occupational epidemiologists. We update the seminal review by Arrighi
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and Hertz-Picciotto (Epidemiology. 1994; 5: 186-196) of the sources and
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methods to control healthy worker survivor bias. In our update, we
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discuss methodologic advances since the publication of that review,
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notably with a consideration of how directed acyclic graphs can inform
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the choice of appropriate analytic methods. We summarize and discuss
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methods for addressing this bias, including recent work applying
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g-methods to account for employment status as a time-varying covariate
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affected by prior exposure. In the presence of healthy worker survivor
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bias, g-methods have advantages for estimating less biased parameters
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that have direct policy implications and are clearly communicated to
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decision-makers.'
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affiliation: 'Buckley, JP (Corresponding Author), Univ N Carolina, Dept Epidemiol,
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CB 7435, Chapel Hill, NC 27599 USA.
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Buckley, Jessie P.; Keil, Alexander P.; McGrath, Leah J.; Edwards, Jessie K., Univ
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N Carolina, Dept Epidemiol, Gillings Sch Global Publ Hlth, Chapel Hill, NC 27599
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USA.
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McGrath, Leah J., RTI Hlth Solut, Chapel Hill, NC USA.'
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author: Buckley, Jessie P. and Keil, Alexander P. and McGrath, Leah J. and Edwards,
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Jessie K.
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author-email: jessbuck@unc.edu
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author_list:
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- family: Buckley
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given: Jessie P.
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- family: Keil
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given: Alexander P.
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- family: McGrath
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given: Leah J.
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- family: Edwards
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given: Jessie K.
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da: '2023-09-28'
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doi: 10.1097/EDE.0000000000000217
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eissn: 1531-5487
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files: []
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issn: 1044-3983
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journal: EPIDEMIOLOGY
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keywords-plus: 'LUNG-CANCER MORTALITY; OCCUPATIONAL ASBESTOS EXPOSURE;
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FAILURE-TIME-MODELS; ACTIVE ANTIRETROVIRAL THERAPY; MARGINAL STRUCTURAL
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MODELS; PARAMETRIC G-FORMULA; MEASUREMENT ERROR; INTERNAL COMPARISONS;
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CUMULATIVE EXPOSURE; CAUSAL INFERENCE'
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language: English
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month: MAR
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number: '2'
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number-of-cited-references: '62'
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orcid-numbers: 'Keil, Alexander/0000-0002-0955-6107
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Edwards, Jessie/0000-0002-1741-335X
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Buckley, Jessie/0000-0001-7976-0157'
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pages: 204-212
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papis_id: c1ceb9bc0c2c49bf8c06742e587c3b26
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ref: Buckley2015evolvingmethods
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researcherid-numbers: 'Keil, Alexander/CAE-8705-2022
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'
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tags:
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- review
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times-cited: '70'
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title: Evolving Methods for Inference in the Presence of Healthy Worker Survivor Bias
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2023-10-01 08:15:07 +00:00
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type: article
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2023-09-28 14:46:10 +00:00
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unique-id: WOS:000349400300026
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usage-count-last-180-days: '0'
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usage-count-since-2013: '16'
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volume: '26'
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web-of-science-categories: Public, Environmental \& Occupational Health
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year: '2015'
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