Assessment and Indirect Adjustment for Confounding by Smoking in Cohort Studies Using Relative Hazards Models
Open accessAbstract
Workers' smoking histories are not measured in many occupational cohort studies. Here we discuss the use of negative control outcomes to detect and adjust for confounding in analyses that lack information on smoking. We clarify the assumptions necessary to detect confounding by smoking and the additional assumptions necessary to indirectly adjust for such bias. We illustrate these methods using data from 2 studies of radiation and lung cancer: the Colorado Plateau cohort study (1950-2005) of underground uranium miners (in which smoking was measured) and a French cohort study (1950-2004) of nuclear industry workers (in which smoking was unmeasured). A cause-specific relative hazards model is proposed for estimation of indirectly adjusted associations. Among the miners, the proposed method suggests no confounding by smoking of the association between radon and lung cancer--a conclusion supported by adjustment for measured smoking. Among the nuclear workers, the proposed method suggests substantial confounding by smoking of the association between radiation and lung cancer. Indirect adjustment for confounding by smoking resulted in an 18% decrease in the adjusted estimated hazard ratio, yet this cannot be verified because smoking was unmeasured. Assumptions underlying this method are described, and a cause-specific proportional hazards model that allows easy implementation using standard software is presented.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In that situation, the difference of the negative control results from the null can be used as an indicator of the extent of bias in the original study.
“In that situation, the difference of the negative control results from the null can be used as an indicator of the extent of bias in the original study.41,42”
From Triangulation in aetiological epidemiology (2017) · cites this paper as [41]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Cancer Epidemiology and Prevention
Schottenfeld, D. (2006) · doi:10.1093/acprof:oso/9780195149616.001.0001 · Cited by 3,376 · see citing works
Mortality in relation to smoking: 50 years' observations on male British doctors
Doll, R., Peto, R., Boreham, J., & Sutherland, I. (2004) · BMJ · doi:10.1136/bmj.38142.554479.ae · Cited by 2,920 · see citing works
Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies
Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181d61eeb · Cited by 1,583 · see citing works
Competing Risk Regression Models for Epidemiologic Data
Lau, B., Cole, S. R., & Gange, S. J. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwp107 · Cited by 1,405 · see citing works
Self-citationHernán, M. Á. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181c1ea43 · Cited by 1,367 · see citing works
Mortality in relation to smoking: 40 years' observations on male British doctors
Doll, R., Peto, R., Wheatley, K., Gray, R., & Sutherland, I. (1994) · BMJ · doi:10.1136/bmj.309.6959.901 · Cited by 1,321 · see citing works
Applying Cox Regression to Competing Risks
Lunn, M., & McNeil, D. (1995) · Biometrics · doi:10.2307/2532940 · Cited by 1,017 · see citing works
Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions
Cornfield, J., Haenszel, W. M., Hammond, E., Lilienfeld, A. M., Shimkin, M. B., & Wynder, E. L. (1959) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/22.1.173 · Cited by 842 · see citing works
Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
Greenland, S. (2003) · Epidemiology · doi:10.1097/01.ede.0000042804.12056.6c · Cited by 810 · see citing works
Illustrating bias due to conditioning on a collider
Cole, S. R., Platt, R. W., Schisterman, E. F., Chu, H., Westreich, D., Richardson, D., & Poole, C. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyp334 · Cited by 796 · see citing works
Self-citationBritish journal of industrial medicine
(1989) · Journal of Sound and Vibration · doi:10.1016/0022-460x(89)90628-7 · Cited by 750 · see citing works
Jacob, S., Bovéda, S., Bar, O., et al. (2012) · International Journal of Cardiology · doi:10.1016/j.ijcard.2012.04.124 · Cited by 250 · see citing works
Indirect methods of assessing the effects of tobacco use in occupational studies
Axelson, O., & Steenland, K. (1988) · American Journal of Industrial Medicine · doi:10.1002/ajim.4700130107 · Cited by 244 · see citing works
Steenland, K. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh211 · Cited by 208 · see citing works
Imputation for Exposure Histories with Gaps, under an Excess Relative Risk Model
Weinberg, C. R., Moledor, E., Umbach, D. M., & Sandler, D. P. (1996) · Epidemiology · doi:10.1097/00001648-199609000-00007 · Cited by 178 · see citing works
COMPONENTS OF THE CRUDE RISK RATIO
Miettinen, O. S. (1972) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a121443 · Cited by 169 · see citing works
Schubauer‐Berigan, M. K., Daniels, R. D., & Pinkerton, L. E. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwn406 · Cited by 145 · see citing works
Self-citationThe Control Outcome Calibration Approach for Causal Inference With Unobserved Confounding
Tchetgen Tchetgen, E. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwt303 · Cited by 119 · see citing works
Radon and COPD mortality in the American Cancer Society Cohort
Turner, M. C., Krewski, D. R., Chen, Y., Pope, C. A., Gapstur, S. M., & Thun, M. J. (2011) · European Respiratory Journal · doi:10.1183/09031936.00058211 · Cited by 99 · see citing works
Methods of control for smoking in occupational cohort mortality studies.
Steenland, K., Beaumont, J., & Halperin, W. (1984) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.2349 · Cited by 86 · see citing works
Metz-Flamant, C., Laurent, O., Samson, É., Caër-Lorho, S., Acker, A., Hubert, P., Richardson, D. B., & Laurier, D. (2013) · Occupational and Environmental Medicine · doi:10.1136/oemed-2012-101149 · Cited by 76 · see citing works
Self-citationKriebel, D. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh151 · Cited by 66 · see citing works
Indirect corrections for confounding under multiplicative and additive risk models
Gail, M. H., Wacholder, S., & Lubin, J. H. (1988) · American Journal of Industrial Medicine · doi:10.1002/ajim.4700130108 · Cited by 62 · see citing works
Confounding from smoking in occupational epidemiology.
Axelson, O. (1989) · Occupational and Environmental Medicine · doi:10.1136/oem.46.8.505 · Cited by 55 · see citing works
Control of smoking in occupational epidemiologic studies: Methods and needs
Blair, A., Steenland, K., Shy, C. M., et al. (1988) · American Journal of Industrial Medicine · doi:10.1002/ajim.4700130102 · Cited by 36 · see citing works
Silica dust, radon and death from non-malignant respiratory diseases in German uranium miners
Kreuzer, M., Sogl, M., Brüske, I., Möhner, M., Nowak, D., Schnelzer, M., & Walsh, L. (2013) · Occupational and Environmental Medicine · doi:10.1136/oemed-2013-101582 · Cited by 33 · see citing works
Occupational Exposures and Lung Cancer
Richardson, D. B. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c6f7d9 · Cited by 30 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Greenland S. Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology. 1996;7(5):498-501.
Cited by
Sorted by citations
Triangulation in aetiological epidemiology
Lawlor, D. A., Tilling, K., & Davey Smith, G. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyw314 · Cited by 1,129 · see citing works
Leuraud, K., Richardson, D. B., Cardis, E., Daniels, R. D., Gillies, M., O'Hagan, J. A., Hamra, G. B., Haylock, R., Laurier, D., Moissonnier, M., et al. (2015) · The Lancet Haematology · doi:10.1016/s2352-3026(15)00094-0 · Cited by 442 · see citing works
Self-citation
- Authors
- Richardson, D. B., Laurier, D., Schubauer-Berigan, M. K., Tchetgen, E. T., & Cole, S. R.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Richardson, D. B.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-09-21
- Volume
- 180
- Issue
- 9
- Pages
- 933-940
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC4375397
- OpenAlex
- W2122091203
- Cited by
- 51
- References
- 27
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 4 · 2023: 6 · 2022: 5 · 2021: 5 · 2020: 5 · 2019: 3 · 2018: 1 · 2017: 7 · 2016: 2 · 2015: 5
- FWCI
- 4.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- HAL (submittedVersion)
- UNC Libraries
- Funders
- Areva
- Harvard University
- European Commission
- Electricité de France
- University of North Carolina at Chapel Hill
- Fifth Framework Programme
- National Institute of Allergy and Infectious Diseases (R01AI104459)
- National Institute of Environmental Health Sciences (R01ES020337)
- National Institute for Occupational Safety and Health
- 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
- smoking confounding, indirect adjustment, negative control outcomes, smoking history, occupational cohort studies, radon exposure, lung cancer, uranium miners, nuclear workers, radiation exposure, unmeasured confounding
- MeSH terms
- Epidemiologic Methods, Humans, Lung Neoplasms, Mining, Neoplasms, Radiation-Induced, Retrospective Studies, Smoking, Social Class, Uranium, Cohort Studies, Confounding Factors, Epidemiologic, Proportional Hazards Models, Occupational Exposure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
