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Cited by 305Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyGlobal Cancer Incidence and Screening

A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods

Robins, James·1987·Journal of Chronic Diseases
DOI 10.1016/s0021-9681(87)80018-8
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Abstract

In observational cohort mortality studies with prolonged periods of exposure to the agent under study, independent risk factors for death commonly determine subsequent exposure to the study agent. For example, in occupational mortality studies, date of termination of employment is both a determinant of subsequent exposure to the chemical agent under study (since terminated individuals receive no further exposure) and an independent risk factor for death (since disabled individuals tend to leave employment). When a risk factor determines subsequent exposure and is determined by previous exposure, standard analyses that estimate age-specific mortality rates as a function of cumulative exposure can underestimate the true effect of exposure on mortality, whether or not one adjusts for the risk factor in the analysis. This observation raises the question, "Which, if any, empirical population parameter can be causally interpreted as the true effect of exposure in observational mortality studies?" In answer, we offer a graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. We reanalyze the mortality experience of a cohort of arsenic-exposed copper smelter workers using our approach and compare our results with those obtained using standard methods. We find an adverse effect of arsenic exposure on all cause and lung cancer mortality, which standard methods failed to detect. The analytic approach introduced in this paper may be necessary to control bias in any epidemiologic study in which there exists a risk factor which both determines subsequent exposure and is determined by previous exposure to the agent under study.

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  • The expression for the sequential-intervention causal effect coincides with Robins' (1987) G-computation formula, which was derived from a more complicated set of counterfactual assumptions.

    “This expression coincides with Robins' (1987) G -computation formula, which was derived from a more complicated set of (counterfactual) assumptions.”

    From Causal inference in statistics: An overview (2009) · cites this paper as Robins, 1987

  • The expression for the causal effect of a treatment sequence do(X=x), do(Z2=z2) coincides with Robins' (1987) G-computation formula.

    “This expression coincides with Robins' (1987) G-computation formula, which was derived from a more complicated set of (counterfactual) assumptions.”

    From An Introduction to Causal Inference (2010) · cites this paper as Robins' (1987)

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Authors
Robins, J.
Affiliations
Harvard University (US)
Corresponding author
Robins, James
ORCID
Robins, James
Venue
Journal of Chronic Diseases
Publisher
Elsevier BV
Published
1987-01
Volume
40
Pages
139S-161S
ISSN
0021-9681 (ISSN-L)
DOI
10.1016/s0021-9681(87)80018-8
OpenAlex
W2035142205
Cited by
305
References
9
Citations by year
2026: 5 · 2025: 6 · 2024: 6 · 2023: 7 · 2022: 9 · 2021: 11 · 2020: 12 · 2019: 12 · 2018: 11 · 2017: 10 · 2016: 10 · 2015: 12 · 2014: 20 · 2013: 6 · 2012: 11
FWCI
3.73
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Journal of Chronic Diseases (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (5r23es003405-03)
  • National Institute of Environmental Health Sciences (5-P30-ES00002, 5-R23-ES03405, P30ES000002)
  • National Institute for Occupational Safety and Health (5 R03 OH01867)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
Keywords
causal inference, directed acyclic graphs, mortality, cumulative exposure, confounding by indication, occupational exposure, arsenic, copper smelter workers, lung cancer mortality, all-cause mortality, bias control
MeSH terms
Epidemiologic Methods, Humans, Occupational Diseases, Probability, Risk Factors, Statistics as Topic, Time Factors

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