title: Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.
abstract: Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure.
authors.affiliation: Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, U.S.A.
authors.forename: Deukwoo
authors.initials: D
authors.lastname: Kwon
authors.affiliation: Oak Ridge Center for Risk Analysis, Oak Ridge, TN, U.S.A.
authors.forename: F Owen
authors.initials: FO
authors.lastname: Hoffman
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, U.S.A.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Moroz
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, U.S.A.
authors.forename: Steven L
authors.initials: SL
authors.lastname: Simon
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D003198:Computer Simulation; D004307:Dose-Response Relationship, Radiation; D004781:Environmental Exposure / Q000009:adverse effects / Q000706:statistics & numerical data; D015340:Epidemiologic Research Design; D006801:Humans; D007623:Kazakhstan / Q000453:epidemiology; D009010:Monte Carlo Method; D015995:Prevalence; D011847:Radioactive Fallout / Q000009:adverse effects* / Q000706:statistics & numerical data; D011874:Radiometry / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods; D016606:Thyroid Nodule / Q000453:epidemiology* / Q000209:etiology; D035501:Uncertainty
chemical_list: D011847:Radioactive Fallout
keywords: Bayesian model averaging; cancer risk estimation; dose-response model; radiation epidemiology