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Analysis of Multiple Exposures: An Empirical Comparison of Results From Conventional and Semi-Bayes Modeling Strategies

Momoli, Francoa; Abrahamowicz, Michala; Parent, Marie-Éliseb; Krewski, Danc; Siemiatycki, Jackd

doi: 10.1097/EDE.0b013e3181c297c7
Methods: Original Article

Background: Analysts of epidemiologic data often contend with the problem of estimating the independent effects of many correlated exposures. General approaches include assessing each exposure separately, adjusting for some subset of other exposures, or assessing all exposures simultaneously in a single model such as semi-Bayes modeling. The optimal strategy remains uncertain, and it is unclear to what extent different reasonable approaches influence findings. We provide an empirical comparison of results from several modeling strategies.

Methods: In an occupational case-control study of lung cancer with 184 exposure substances, we implemented 6 modeling strategies to estimate odds ratios for each exposure-cancer association. These included one-exposure-at-a-time models with various confounder selection criteria (such as a priori selection or a change-in-the-estimate criterion) and semi-Bayes models, one version of which integrated information on previous evidence and chemical properties.

Results: While distributions of odds ratios were broadly similar across the 6 analytic strategies, there were some differences in point estimates and in substances manifesting statistically significant odds ratios, particularly between strategies with few or no occupational covariates and those with many. Semi-Bayes models produced fewer statistically significant odds ratios than other methods. A simple semi-Bayes model that shrank all the 184 estimates to a common mean yielded nearly identical results to one that integrated considerable prior information.

Conclusion: Different modeling strategies can lead to different results. Considering the conceptual and pragmatic difficulties of identifying confounders, these results suggest that it would be unwise to place uncritical reliance on any single strategy.


From the aDepartment of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada; bINRS-Institut Armand-Frappier, University of Quebec, Laval, QC, Canada; cMcLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada; and dSchool of Public Health, University of Montreal, Montreal, QC, Canada.

Submitted 9 February 2008; accepted 24 June 2009.

Supported by a doctoral research award from the Canadian Institutes of Health Research, and by the National Cancer Institute of Canada through the Program of Research in Environmental Etiology of Cancer (PREECAN) (F.M.); Fonds de recherche en sante du Quebec (M.-E.P.); NSERC Chair in Risk Science (D.K.); and Canada Research Chair and Guzzo-SRC Chair in Environment and Cancer (J.S.).

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Correspondence: Jack Siemiatycki, Research Centre of CHUM, 3875 rue Saint-Urbain, 3rd Floor, Montreal, QC, H2W 1V1, Canada. E-mail:

© 2010 Lippincott Williams & Wilkins, Inc.