Copyright © 2004 by the Johns Hopkins Bloomberg School of Public Health
PRACTICE OF EPIDEMIOLOGY |
Monte Carlo Sensitivity Analysis and Bayesian Analysis of Smoking as an Unmeasured Confounder in a Study of Silica and Lung Cancer
1 Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA.
2 Departments of Epidemiology and Statistics, University of California, Los Angeles, Los Angeles, CA.
Conventional confidence intervals reflect uncertainty due to random error but omit uncertainty due to biases, such as confounding, selection bias, and measurement error. Such uncertainty can be quantified, especially if the investigator has some idea of the amount of such bias. A traditional sensitivity analysis produces one or more point estimates for the exposure effect hypothetically adjusted for bias, but it does not provide a range of effect measures given the likely range of bias. Here the authors used Monte Carlo sensitivity analysis and Bayesian bias analysis to provide such a range, using data from a US silica-lung cancer study in which results were potentially confounded by smoking. After positing a distribution for the smoking habits of workers and referents, a distribution of rate ratios for the effect of smoking on lung cancer, and a model for the bias parameter, the authors derived a distribution for the silica-lung cancer rate ratios hypothetically adjusted for smoking. The original standardized mortality ratio for the silica-lung cancer relation was 1.60 (95% confidence interval: 1.31, 1.93). Monte Carlo sensitivity analysis, adjusting for possible confounding by smoking, led to an adjusted standardized mortality ratio of 1.43 (95% Monte Carlo limits: 1.15, 1.78). Bayesian results were similar (95% posterior limits: 1.13, 1.84). The authors believe that these types of analyses, which make explicit and quantify sources of uncertainty, should be more widely adopted by epidemiologists.
Bayes theorem; confounding factors (epidemiology); epidemiologic methods; lung neoplasms; Monte Carlo method; occupational exposure; occupations; smoking
Abbreviations: Abbreviations: RR, rate ratio; SMR, standardized mortality ratio.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S. Greenland Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods Int. J. Epidemiol., September 9, 2009; (2009) dyp278v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F de Vocht, H Kromhout, G Ferro, P Boffetta, and I Burstyn Bayesian modelling of lung cancer risk and bitumen fume exposure adjusted for unmeasured confounding by smoking Occup. Environ. Med., August 1, 2009; 66(8): 502 - 508. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D Loomis, J M Dement, S H Wolf, and D B Richardson Lung cancer mortality and fibre exposures among North Carolina asbestos textile workers Occup. Environ. Med., August 1, 2009; 66(8): 535 - 542. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L Kheifets, J D Bowman, H Checkoway, M Feychting, J M Harrington, R Kavet, G Marsh, G Mezei, D C Renew, and E van Wijngaarden Future needs of occupational epidemiology of extremely low frequency electric and magnetic fields: review and recommendations Occup. Environ. Med., February 1, 2009; 66(2): 72 - 80. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A M Jurek, G Maldonado, L G Spector, and J A Ross Periconceptional maternal vitamin supplementation and childhood leukaemia: an uncertainty analysis J Epidemiol Community Health, February 1, 2009; 63(2): 168 - 172. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F.-Z. Kettani, A. Dragomir, R. Cote, L. Roy, A. Berard, L. Blais, L. Lalonde, P. Moreau, and S. Perreault Impact of a Better Adherence to Antihypertensive Agents on Cerebrovascular Disease for Primary Prevention Stroke, January 1, 2009; 40(1): 213 - 220. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G Maldonado Adjusting a relative-risk estimate for study imperfections J Epidemiol Community Health, July 1, 2008; 62(7): 655 - 663. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. M Applebaum, E. J Malloy, and E. A Eisen Reducing healthy worker survivor bias by restricting date of hire in a cohort study of Vermont granite workers Occup. Environ. Med., October 1, 2007; 64(10): 681 - 687. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. M Jurek, G. Maldonado, S. Greenland, and T. R Church Uncertainty analysis: an example of its application to estimating a survey proportion J Epidemiol Community Health, July 1, 2007; 61(7): 650 - 654. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. M. Oakes and T. R. Church Invited Commentary: Advancing Propensity Score Methods in Epidemiology Am. J. Epidemiol., May 15, 2007; 165(10): 1119 - 1121. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Pelucchi, E. Pira, G. Piolatto, M. Coggiola, P. Carta, and C. La Vecchia Occupational silica exposure and lung cancer risk: a review of epidemiological studies 1996-2005 Ann. Onc., July 1, 2006; 17(7): 1039 - 1050. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. A. Engels, M. V. Brock, J. Chen, C. M. Hooker, M. Gillison, and R. D. Moore Elevated Incidence of Lung Cancer Among HIV-Infected Individuals J. Clin. Oncol., March 20, 2006; 24(9): 1383 - 1388. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. M Jurek, S. Greenland, G. Maldonado, and T. R Church Proper interpretation of non-differential misclassification effects: expectations vs observations Int. J. Epidemiol., June 1, 2005; 34(3): 680 - 687. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Greenland Model-based Estimation of Relative Risks and Other Epidemiologic Measures in Studies of Common Outcomes and in Case-Control Studies Am. J. Epidemiol., August 15, 2004; 160(4): 301 - 305. [Abstract] [Full Text] [PDF]
|
|