A Bayesian mixture modeling approach for assessing the effects of correlated exposures in case-control studies

Journal of Exposure Science and Environmental Epidemiology

Volumn 22, Issue 4, 2012, Pages 352-360

  De Vocht, Frank ^a   Cherry, Nicola ^b   Wakefield, Jon ^c  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF ALBERTA   (Canada)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

empirical statistical models; epidemiology; exposure modeling; population based studies; volatile organic compounds

Indexed keywords

ARTICLE; BAYES THEOREM; CASE CONTROL STUDY; ENVIRONMENTAL EXPOSURE; LIFESTYLE; THEORETICAL MODEL;

BAYES THEOREM; CASE-CONTROL STUDIES; ENVIRONMENTAL EXPOSURE; LIFE STYLE; MODELS, THEORETICAL;

EID: 84862525228     PISSN: 15590631     EISSN: 1559064X     Source Type: Journal    
DOI: 10.1038/jes.2012.22     Document Type: Article

References (50)

1. Vineis P., Khan A.E., Vlaanderen J., and Vermeulen R. The impact of new research technologies on our understanding of environmental causes of disease: the concept of clinical vulnerability. Environ Health 2009: 8: 54.
2. McNamee R. Regression modelling and other methods to control confounding. Occup Environ Med 2005: 62(7): 500-506.
3. Greenland S., and Robins J.M. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol 1986: 15(3): 413-419.
4. Herring A.H. Nonparametric bayes shrinkage for assessing exposures to mixtures subject to limits of detection. Epidemiology 2010: 21(4): S71-S76.
5. Anderson H.R., Atkinson R.W., Bremner S.A., and Marston L. Particulate air pollution and hospital admissions for cardiorespiratory diseases: are the elderly at greater risk? Eur Respir J Suppl 2003: 40: 39s-46s.
6. Rothman K., and Greenland S. Modern Epidemiology, 2nd edn. Lippincott-Raven, Philadelphia, 1998.
7. Momoli F., Abrahamowicz M., Parent M.E., Krewski D., and Siemiatycki J. Analysis of multiple exposures: an empirical comparison of results from conventional and semi-bayes modeling strategies. Epidemiology 2010: 21(1): 144-151.
8. Smith T.J., and Kriebel D. A Biologic Approach to Environmental Assessment and Epidemiology. Oxford University Press, Inc, New York, 2010.
9. Greenland S. Bayesian perspectives for epidemiological research. II. Regression analysis. Int J Epidemiol 2007: 36(1): 195-202.
10. Conti D.V., Cortessis V., Molitor J., and Thomas D.C. Bayesian modeling of complex metabolic pathways. Hum Hered 2003: 56(1-3): 83-93.
11. Thomas D.C., Witte J.S., and Greenland S. Dissecting effects of complex mixtures: who's afraid of informative priors? Epidemiology 2007: 18(2): 186-190.
12. Sahmel J., Devlin K., Paustenbach D., Hollins D., and Gaffney S. The role of exposure reconstruction in occupational human health risk assessment: current methods and a recommended framework. Crit Rev Toxicol 2010: 40(9): 799-843.
13. MacLehose R.F., Dunson D.B., Herring A.H., and Hoppin J.A. Bayesian methods for highly correlated exposure data. Epidemiology 2007: 18(2): 199-207.
14. Gelman A., Carlin J., Stern H., and Rubin D. Bayesian Data Analysis, 2 edn. Chapman and Hall/CRC, New York, 2004.
15. Wakefield J., de Vocht F., and Hung R.J. Bayesian mixture modeling of gene-environment and gene-gene interactions. Genet Epidemiol 2010: 34(1): 16-25.
16. Greenland S. Methods for epidemiologic analyses of multiple exposures: a review and comparative study of maximum-likelihood, preliminary-testing, and empirical- Bayes regression. Stat Med 1993: 12(8): 717-736.
17. Thomas D.C., Siemiatycki J., Dewar R., Robins J., Goldberg M., and Armstrong B.G. The problem of multiple inference in studies designed to generate hypotheses. Am J Epidemiol 1985: 122(6): 1080-1095.
18. Mitchell T.J., and Beauchamp J.J. Bayesian variable selection in linear regression (with discussion). J Am Stat Assoc 1988: 83: 1023-1036.
19. George E.I., and McCulloch R.E. Variable selection via Gibbs sampling. J Am Stat Assoc 1993: 88: 881-889.
20. Chipman H. Bayesian variable selection with related predictors. Can J Stat 1996: 24: 17-36.
21. Cherry N., Moore H., McNamee R., Pacey A., Burgess G., and Clyma J.A.,et al. Occupation and male infertility: glycol ethers and other exposures. Occup Environ Med 2008: 65: 708-714.
22. Greenland S. Invited commentary: variable selection versus shrinkage in the control of multiple confounders. Am J Epidemiol 2008: 167(5): 523-529.
23. Gilks W.R., Richardson S., and Spiegelhalter D.J. Markov Chain Monte Carlo in Practice, 1 edn. Chapman & Hall/CRC, London, 1996.
24. Barman S.C., Kumar N., Singh R., Kisku G.C., Khan A.H., and Kidwai M.M.,et al. Assessment of urban air pollution and it's probable health impact. J Environ Biol 2010: 31(6): 913-920.
25. Franck U., Herbarth O., Roder S., Schlink U., Borte M., and Diez U.,et al. Respiratory effects of indoor particles in young children are size dependent. Sci Total Environ 2011: 409(9): 1621-1631.
26. Wang Y., Hopke P.K., Chalupa D.C., and Utell M.J. Long-term characterization of indoor and outdoor ultrafine particles at a commercial building. Environ Sci Technol 2010: 44(15): 5775-5780.
27. Sobus J.R., McClean M.D., Herrick R.F., Waidyanatha S., Onyemauwa F., and Kupper L.L.,et al. Investigation of PAH biomarkers in the urine of workers exposed to hot asphalt. Ann Occup Hyg 2009: 53(6): 551-560.
28. Ioannidis J.P.A., Tarone R., and McLaughlin J.K. The false-positive to false-negative ratio in epidemiological studies. Epidemiology 2011: 22(4): 450-456.
29. Thomas D., Langholz B., Clayton D., Pitkaniemi J., Tuomilehto-Wolf E., and Tuomilehto J. Empirical Bayes methods for testing associations with large numbers of candidate genes in the presence of environmental risk factors, with applications to HLA associations in IDDM. Ann Med 1992: 24(5): 387-392.
30. Papathomas M., Molitor J., Richardson S., Riboli E., and Vineis P. Examining the joint effect of multiple risk factors using exposure risk profiles: lung cancer in nonsmokers. Environ Health Perspect 2011: 119(1): 84-91.
31. Lunn D.J., Thomas A., Best N., and Spiegelhalter D. WINBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 2000: 10: 325-337.
32. Cherry N., Labrèche F., Collins J., and Tulandi T. Occupational exposure to solvents and male fertility. Occup Environ Med 2001: 58(10): 635-640.
33. Multigner L., Ben Brik E., Arnaud I., Haguenoer J.M., Jouannet P., and Auger J.,et al. Glycol ethers and semen quality: a cross-sectional study among male workers in the Paris Municipality. Occup Environ Med 2007: 64(7): 467-473.
34. Iorio R., Scrimaglio R., Rantucci E., Delle Monache S., Di Gaetano A., and Finetti N.,et al. A preliminary study of oscillating electromagnetic field effects on human spermatozoon motility. Bioelectromagnetics 2007: 28(1): 72-75.
35. Boffetta P., McLaughlin J.K., La Veccia C., Tarone R.E., Lipworth L., and Blot W.J. False-positive results in cancer epidemiology: a plea for epistemological modesty. J Natl Cancer Inst 2008: 100(14): 988-995.
36. Boffetta P., McLaughlin J.K., La Veccia C., Tarone R.E., Lipworth L., and Blot W.J. A further plea for adherence to the principles underlying science in general and the epidemiologic enterprise in particular. Int J Epidemiol 2009: 38(3): 678-679.
37. Blair A., Saracci R., Vineis P., Cocco P., Forastiere F., and Grandjean P.,et al. Epidemiology, public health, and the rhetoric of false positives. Environ Health Perspect 2009: 117(12): 1809-1813.
38. Clapp R.W., and Kriebel D. Re: False-positive results in cancer epidemiology: a plea for epistemological modesty. J Natl Cancer Inst 2009: 101(3): 211-212.
39. Yuspa S.H. The pathogenesis of squamous cell cancer: lessons learned from studies of skin carcinogenesis-thirty-third G. H. A. Clowes Memorial Award Lecture. Cancer Res 1994: 54(5): 1178-1189.
40. Gustafson P., and Burstyn I. Bayesian inference of gene-environment interaction from incomplete data: what happens when information on environment is disjoint from data on gene and disease? Stat Med 2011: 30(8): 877-889.
41. Burstyn I., Kim H.M., Yasui Y., and Cherry N.M. The virtues of a deliberately misspecified disease model in demonstrating a gene-environment interaction. Occup Environ Med 2009: 66(6): 374-380.
42. Gustafson P. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments, 1 edn. Chapman and Hall/CRC, New York, 2003.
43. Peters S., Vermeulen R., Cassidy A., Mannetje A.T., van Tongeren M., and Boffetta P.,et al. Comparison of exposure assessment methods for occupational carcinogens in a multi-centre lung cancer case-control study. Occup Environ Med 2011: 68(2): 148-153.
44. Day G.A., Esmen N.A., and Hall T.A. Sample size-based indication of normality in lognormally distributed populations. Appl Occup Environ Hyg 1999: 14(6): 376-383.
45. Hung R.J., Brennan P., Malaveille C., Porru S., Donato F., and Boffetta P.,et al. Using hierarchical modeling in genetic association studies with multiple markers: application to a case-control study of bladder cancer. Cancer Epidemiol Biomarkers Prev 2004: 13(6): 1013-1021.
46. Green P. Reversible jump MCMC computation and Bayesian model determination. Biometrika 1995: 82: 711-732.
47. Chu R., Gustafson P., and Le N. Bayesian adjustment for exposure misclassification in case-control studies. Stat Med 2010: 29: 994-1003.
48. Espino-Hernandez G., Gustafson P., and Burstyn I. Bayesian adjustment for measurement error in continuous exposures in an individually matched casecontrol study. BMC Med Res Methodol 2011: 14: 11-67.
49. Clayton D.G. Models for the analysis of cohort and case-control studies with inaccurately measured exposures. In: J. H. Dwyeret al. (Eds) Statistical Models for Longitudinal Studies of Exposure and Health. Oxford University Press, New York, 1992: 16, pp. 301-331.
50. Richardson S., and Gilks W.R. Conditional independence models for epidemiological studies with covariate measurement error. Stat Med 1993: 12(18): 1703-1722.