Incorporating individual-level distributions of exposure error in epidemiologic analyses: An example using arsenic in drinking water and bladder cancer

Annals of Epidemiology

Volumn 20, Issue 10, 2010, Pages 750-758

  Meliker, Jaymie R ^a   Goovaerts, Pierre ^b   Jacquez, Geoffrey M ^b,c   Nriagu, Jerome O ^c  

^a STONY BROOK UNIVERSITY   (United States)

^b BIOMEDWARE INC   (United States)

^c UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Age Factors; Arsenicals; Environmental Exposure; Epidemiologic Methods; Monte Carlo Method; Residential Mobility; Uncertainty; Urinary Bladder

Indexed keywords

ARSENIC; DRINKING WATER; ORGANOARSENIC DERIVATIVE;

ARTICLE; BLADDER CANCER; EXPOSURE; HUMAN; MONTE CARLO METHOD; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RISK ASSESSMENT; SENSITIVITY ANALYSIS; ANALYSIS; ARSENIC POISONING; CHEMICALLY INDUCED; COMPLICATION; ENVIRONMENTAL EXPOSURE; MIDDLE AGED; PROBABILITY; SENSITIVITY AND SPECIFICITY; UNITED STATES; URINARY BLADDER NEOPLASMS; WATER SUPPLY;

ARSENIC POISONING; ARSENICALS; CONFOUNDING FACTORS (EPIDEMIOLOGY); ENVIRONMENTAL EXPOSURE; EPIDEMIOLOGIC METHODS; HUMANS; MICHIGAN; MIDDLE AGED; PROBABILITY; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; URINARY BLADDER NEOPLASMS; WATER SUPPLY;

EID: 77956253927     PISSN: 10472797     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.annepidem.2010.06.012     Document Type: Article

References (47)

1. Hatch M., Thomas D. Measurement issues in environmental epidemiology. Environ Health Perspect 1993, 101(Suppl 4):49-57.
2. Prentice R.L., Thomas D. Methodologic research needs in environmental epidemiology: data analysis. Environ Health Perspect 1993, 101(Suppl 4):39-48.
3. Rothman K. Methodological frontiers in environmental epidemiology. Environ Health Perspect 1993, 101(Suppl 4):19-21.
4. Dosemeci M., Wacholder S., Lubin J.H. Does nondifferential misclassification of exposure always bias a true effect toward the null value. Am J Epidemiol 1990, 132:746-748.
5. Flegal K.M., Keyl P.M., Nieto F.J. Differential misclassification arising from nondifferential errors in exposure measurement. Am J Epidemiol 1991, 134:1233-1244.
6. Jurek A.M., Greenland S., Maldonado G. How far from non-differential does exposure or disease misclassification have to be to bias results away from the null?. Int J Epidemiol 2008, 37:382-385.
7. Jurek A.M., Greenland S., Maldonado G., Church T.R. Proper interpretation of non-differential misclassification effects: expectations vs observations. Int J Epidemiol 2005, 34:680-687.
8. Wacholder S., Dosemeci M., Lubin J.H. Blind assignment of exposure does not always prevent differential misclassification. Am J Epidemiol 1991, 134:433-437.
9. Thomas D., Stram D., Dwyer J. Exposure measurement error: influence on exposure-disease. Relationships and methods of correction. Annu Rev Public Health 1993, 14:69-93.
10. Maldonado G. Adjusting a relative-risk estimate for study imperfections. J Epidemiol Commun H 2008, 62:655-663.
11. Barron B.A. The effects of misclassification on the estimation of relative risk. Biometrics 1977, 33:414-417.
12. Chu H., Wang Z., Cole S.R., Greenland S. Sensitivity analysis of misclassification: a graphical and a Bayesian approach. Ann Epidemiol 2006, 16:834-841.
13. Fox M.P., Lash T.L., Greenland S. A method to automate probabilistic sensitivity analyses of misclassified binary variables. Int J Epidemiol 2005, 34:1370-1376.
14. Gustafson P., Greenland S. Curious phenomena in Bayesian adjustment for exposure misclassification. Stat Med 2006, 25:87-103.
15. Orsini N., Bellocco R., Bottai M., Wolk A., Greenland S. A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies. Stata J 2008, 8:29-48.
16. Reade-Christopher J., Kupper L.L. Effects of exposure misclassification on regression analyses of epidemiologic follow-up study data. Biometrics 1991, 47:535-548.
17. Weinkam J.J., Rosenbaum W.L., Sterling T.D. A practical approach to estimating the true effect of exposure despite imprecise exposure classification. Am J Ind Med 1991, 19:587-601.
18. Hoffman F.O., Ruttenber J., Apostoaei A.I., Carroll R.J., Greenland S. The Hanford Thyroid Disease Study: an alternative view of the findings. Health Phys 2007, 92:99-111.
19. Li Y., Guolo A., Hoffmann J., Carroll R.J. Shared uncertainty in measurement error problems, with application to the Nevada Test Site fallout data. Biometrics 2007, 63:1226-1236.
20. Lyon J.L., Alder S.C., Stone M.B., Scholl A., Reading J.C., Holubkov R., et al. Thyroid disease associated with exposure to the Nevada nuclear weapons test site radiation: a reevaluation based on corrected dosimetry and examination data. Epidemiology 2006, 17:604-614.
21. Mallick B., Hoffman F.O., Carroll R.J. Semiparametric regression modeling with mixtures of Berkson and classical error, with application to fallout from the Nevada test site. Biometrics 2002, 58:13-20.
22. Schafer D.W., Gilbert E.S. Some statistical implications of dose uncertainty in radiation dose-response analyses. Radiat Res 2006, 166:303-312.
23. Schafer D.W., Lubin J.H., Ron E., Stovall M., Carroll R.J. Thyroid cancer following scalp irradiation: A reanalysis accounting for uncertainty in dosimetry. Biometrics 2001, 57:689-697.
24. Spiegelman D., Valanis B. Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists. Am J Pub Health 1998, 88:406-412.
25. Stefanski L.A., Cook J. Simulation extrapolation: the measurement error jackknife. J Am Stat Assoc 1995, 90:1247-1256.
26. Stürmer T., Thürigen D., Spiegelman D., Blettner M., Brenner H. The performance of methods for correcting measurement error in case-control studies. Epidemiology 2002, 13:507-516.
27. Van Roosbroeck S., Li R., Hoek G., Lebret E., Brunekreef B., Spiegelman D. Traffic-related outdoor air pollution and respiratory symptoms in children: the impact of adjustment for exposure measurement error. Epidemiology 2008, 19:409-416.
28. Armstrong B.G. Effect of measurement error on epidemiological studies of environmental and occupational exposures. Occup Environ Med 1998, 55:651-656.
29. Lash T.L., Fink A.K. Semi-automated sensitivity analysis to assess systematic errors in observational data. Epidemiology 2003, 14:451-458.
30. Greenland S. Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods. Int J Epidemiol 2009, 38:1662-1673.
31. Gustafson P., McCandless L.C. Probabilistic approaches to better quantifying the results of epidemiologic studies. Int J Environ Res Public Health 2010, 7:1520-1539.
32. Kopecky K.J., Davis S., Hamilton T.E., Saporito M.S., Onstad L.E. Estimation of thyroid radiation doses for the Hanford thyroid disease study: results and implications for statistical power of the epidemiological analyses. Health Phys 2004, 87:15-32.
33. Stayner L., Vrijheid M., Cardis E., Stram D.O., Deltour I., Gilbert S.J., et al. A Monte Carlo maximum likelihood method for estimating uncertainty arising from shared errors in exposures in epidemiological studies of nuclear workers. Radiat Res 2007, 168:757-763.
34. Stevens W., Thomas D.C., Lyon J.L., Till J.E., Kerber R.A., Simon S.L., et al. Leukemia in Utah and radioactive fallout from the Nevada test site. A case-control study. JAMA 1990, 264:585-591.
35. French N.H.F., Goovaerts P., Kasischke E.S. Uncertainty in estimating carbon emissions from boreal forest fires. J Geophys Res 2004, 109. D14S08.
36. Greenland S. Multiple-bias modeling for analysis of observational data. J R Stat Soc A 2005, 168:267-306.
37. Waller L.A., Gotway C.A. Applied Spatial Statistics for Public Health Data 2004, John Wiley and Sons, Hoboken (NJ), 406-409.
38. Meliker J.R., Slotnick M.J., AvRuskin G.A., Schottenfeld D., Jacquez G.M., Wilson M.L., et al. Lifetime exposure to arsenic in drinking water and bladder cancer: a population-based case-control study in Michigan. Cancer Cause Control 2010, 21:745-757.
39. Slotnick M.J., Meliker J.R., Nriagu J.O. Effects of time and point-of-use devices on arsenic levels in southeastern Michigan drinking water, USA. Sci Total Environ 2006, 369:42-50.
40. Goovaerts P., AvRuskin G., Meliker J., Slotnick M., Jacquez G., Nriagu J. Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan. Water Resources Res 2005, 41. W07013.
41. Meliker J.R., AvRuskin G.A., Slotnick M.J., Goovaerts P., Schottenfeld D., Jacquez G.M., et al. Validity of spatial models of arsenic concentrations in private well water. Environ Res 2008, 106:42-50.
42. USGS. Arsenic in Groundwater of the United States. Data updated March 30, 2000. Available at: Accessed September 20, 2005. http://water.usgs.gov/nawqa/trace/arsenic/.
43. Natural Resources Defense Council Arsenic and Old Laws: A Scientific and Public Health Analysis of Arsenic Occurrence in Drinking Water, Its Health Effects, and EPA's Outdated Arsenic Tap Water Standard. 2000 Available at:, Accessed September 20, 2005. http://www.nrdc.org/water/drinking/arsenic/aolinx.asp.
44. Kobrosly R.W., Meliker J.R., Nriagu J.O. Automobile industry occupations and bladder cancer: a population-based case-control study in southeastern Michigan. USA. Occup Environ Med 2009, 66:650-656.
45. Silverman D.T., Devesa S.S., Moore L.E., Rothman N. Bladder cancer. Cancer Epidemiology and Prevention 2006, 1101-1127. Oxford University Press, New York (NY). 3rd ed. D. Schottenfeld, J. Fraumeni (Eds.).
46. Rothman K. Induction and latent periods. Am J Epidemiol 1981, 114:253-259.
47. Hofer E. How to account for uncertainty due to measurement errors in an uncertainty analysis using Monte Carlo simulation. Health Phys 2008, 95:277-290.