Bayesian modelling of lung cancer risk and bitumen fume exposure adjusted for unmeasured confounding by smoking.
OBJECTIVES: Residual confounding can be present in epidemiological studies because information on confounding factors was not collected. A Bayesian framework, which has the advantage over frequentist methods that the uncertainty in the association between the confounding factor and exposure and dise...
| Published in: | Occupational & Environmental Medicine Vol. 66; no. 8; pp. 502 - 509 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
BMJ Publishing Group
Aug2009
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105385269&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105385269 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13510711 22H jtl: Occupational & Environmental Medicine issn: 13510711 maglogo: N pubinfo: dt: Aug2009 vid: 66 iid: 8 pid: 8280 pub: BMJ Publishing Group artinfo: ui: 105385269 2010347077 10.1136/oem.2008.042606 NLM19060029 105385269 ppf: 502 ppct: 7 formats: tig: atl: Bayesian modelling of lung cancer risk and bitumen fume exposure adjusted for unmeasured confounding by smoking. aug: au: de Vocht F Kromhout H Ferro G Boffetta P Burstyn I affil: Frank de Vocht, Occupational and Environmental Health Research Group, School of Translational Medicine, Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 8GE, UK; frank.devocht@manchester.ac.uk. sug: subj: Hydrocarbons Lung Neoplasms Etiology Occupational Diseases Etiology Occupational Exposure Adverse Effects Probability Smoking Air Pollutants, Occupational Bias (Research) Confounding Variable Environmental Exposure Europe Israel Lung Neoplasms Epidemiology Male Occupational Diseases Epidemiology Occupational Exposure Prospective Studies Relative Risk Smoke Adverse Effects Smoking Epidemiology Human Male ab: OBJECTIVES: Residual confounding can be present in epidemiological studies because information on confounding factors was not collected. A Bayesian framework, which has the advantage over frequentist methods that the uncertainty in the association between the confounding factor and exposure and disease can be reflected in the credible intervals of the risk parameter, is proposed to assess the magnitude and direction of this bias. METHODS: To illustrate this method, bias from smoking as an unmeasured confounder in a cohort study of lung cancer risk in the European asphalt industry was assessed. A Poisson disease model was specified to assess lung cancer risk associated with career average, cumulative and lagged bitumen fume exposure. Prior distributions for the exposure strata, as well as for other covariates, were specified as uninformative normal distributions. The priors on smoking habits were specified as Dirichlet distributions based on smoking prevalence estimates available for a sub-cohort and assumptions about precision of these estimates. RESULTS: Median bias in this example was estimated at 13%, and suggested an attenuating effect on the original exposure-disease associations. Nonetheless, the results still implied an increased lung cancer risk, especially for average exposure. CONCLUSIONS: This Bayesian framework provides a method to assess the bias from an unmeasured confounding factor taking into account the uncertainty surrounding the estimate and from random sampling error. Specifically for this example, the bias arising from unmeasured smoking history in this asphalt workers' cohort is unlikely to explain the increased lung cancer risk associated with average bitumen fume exposure found in the original study. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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