abstract: 'Healthy worker survivor bias may occur in occupational studies due to

  the tendency for unhealthy individuals to leave work earlier, and

  consequently accrue less exposure, compared with their healthier

  counterparts. If occupational data are not analyzed using appropriate

  methods, this bias can result in attenuation or even reversal of the

  estimated effects of exposures on health outcomes. Recent advances in

  computing power, coupled with state-of-the-art statistical methods, have

  greatly increased the ability of analysts to control healthy worker

  survivor bias. However, these methods have not been widely adopted by

  occupational epidemiologists. We update the seminal review by Arrighi

  and Hertz-Picciotto (Epidemiology. 1994; 5: 186-196) of the sources and

  methods to control healthy worker survivor bias. In our update, we

  discuss methodologic advances since the publication of that review,

  notably with a consideration of how directed acyclic graphs can inform

  the choice of appropriate analytic methods. We summarize and discuss

  methods for addressing this bias, including recent work applying

  g-methods to account for employment status as a time-varying covariate

  affected by prior exposure. In the presence of healthy worker survivor

  bias, g-methods have advantages for estimating less biased parameters

  that have direct policy implications and are clearly communicated to

  decision-makers.'
affiliation: 'Buckley, JP (Corresponding Author), Univ N Carolina, Dept Epidemiol,
  CB 7435, Chapel Hill, NC 27599 USA.

  Buckley, Jessie P.; Keil, Alexander P.; McGrath, Leah J.; Edwards, Jessie K., Univ
  N Carolina, Dept Epidemiol, Gillings Sch Global Publ Hlth, Chapel Hill, NC 27599
  USA.

  McGrath, Leah J., RTI Hlth Solut, Chapel Hill, NC USA.'
author: Buckley, Jessie P. and Keil, Alexander P. and McGrath, Leah J. and Edwards,
  Jessie K.
author-email: jessbuck@unc.edu
author_list:
- family: Buckley
  given: Jessie P.
- family: Keil
  given: Alexander P.
- family: McGrath
  given: Leah J.
- family: Edwards
  given: Jessie K.
da: '2023-09-28'
doi: 10.1097/EDE.0000000000000217
eissn: 1531-5487
files: []
issn: 1044-3983
journal: EPIDEMIOLOGY
keywords-plus: 'LUNG-CANCER MORTALITY; OCCUPATIONAL ASBESTOS EXPOSURE;

  FAILURE-TIME-MODELS; ACTIVE ANTIRETROVIRAL THERAPY; MARGINAL STRUCTURAL

  MODELS; PARAMETRIC G-FORMULA; MEASUREMENT ERROR; INTERNAL COMPARISONS;

  CUMULATIVE EXPOSURE; CAUSAL INFERENCE'
language: English
month: MAR
number: '2'
number-of-cited-references: '62'
orcid-numbers: 'Keil, Alexander/0000-0002-0955-6107

  Edwards, Jessie/0000-0002-1741-335X

  Buckley, Jessie/0000-0001-7976-0157'
pages: 204-212
papis_id: c1ceb9bc0c2c49bf8c06742e587c3b26
ref: Buckley2015evolvingmethods
researcherid-numbers: 'Keil, Alexander/CAE-8705-2022

  '
tags:
- review
times-cited: '70'
title: Evolving Methods for Inference in the Presence of Healthy Worker Survivor Bias
type: article
unique-id: WOS:000349400300026
usage-count-last-180-days: '0'
usage-count-since-2013: '16'
volume: '26'
web-of-science-categories: Public, Environmental \& Occupational Health
year: '2015'