title: Shared dosimetry error in epidemiological dose-response analyses.
abstract: Radiation dose reconstruction systems for large-scale epidemiological studies are sophisticated both in providing estimates of dose and in representing dosimetry uncertainty. For example, a computer program was used by the Hanford Thyroid Disease Study to provide 100 realizations of possible dose to study participants. The variation in realizations reflected the range of possible dose for each cohort member consistent with the data on dose determinates in the cohort. Another example is the Mayak Worker Dosimetry System 2013 which estimates both external and internal exposures and provides multiple realizations of "possible" dose history to workers given dose determinants. This paper takes up the problem of dealing with complex dosimetry systems that provide multiple realizations of dose in an epidemiologic analysis. In this paper we derive expected scores and the information matrix for a model used widely in radiation epidemiology, namely the linear excess relative risk (ERR) model that allows for a linear dose response (risk in relation to radiation) and distinguishes between modifiers of background rates and of the excess risk due to exposure. We show that treating the mean dose for each individual (calculated by averaging over the realizations) as if it was true dose (ignoring both shared and unshared dosimetry errors) gives asymptotically unbiased estimates (i.e. the score has expectation zero) and valid tests of the null hypothesis that the ERR slope β is zero. Although the score is unbiased the information matrix (and hence the standard errors of the estimate of β) is biased for β≠0 when ignoring errors in dose estimates, and we show how to adjust the information matrix to remove this bias, using the multiple realizations of dose. The use of these methods in the context of several studies including, the Mayak Worker Cohort, and the U.S. Atomic Veterans Study, is discussed.
authors: Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America.
authors: Daniel O
authors: DO
authors: Stram
authors: Hirosoft International, Eureka, California, United States of America.
authors: Dale L
authors: DL
authors: Preston
authors: Southern Urals Biophysics Institute, Ozersk, Russia.
authors: Mikhail
authors: M
authors: Sokolnikov
authors: Pacific Northwest National Laboratory, Richland, Washington, United States of America.
authors: Bruce
authors: B
authors: Napier
authors: Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
authors: Kenneth J
authors: KJ
authors: Kopecky
authors: Vanderbilt University, Nashville, Tennessee, United States of America.
authors: John
authors: J
authors: Boice
authors: U.S. Department of Energy, New York, New York, United States of America.
authors: Harold
authors: H
authors: Beck
authors: Risk Assessment Corporation, Neeses, South Carolina, United States of America.
authors: John
authors: J
authors: Till
authors: Radiation Epidemiology Branch, National Cancer Institute, Rockville, Maryland, United States of America.
authors: Andre
authors: A
authors: Bouville
mesh_terms: D004307:Dose-Response Relationship, Radiation; D016021:Epidemiologic Studies; D006801:Humans; D019300:Medical Errors / Q000517:prevention & control / Q000706:statistics & numerical data*; D009010:Monte Carlo Method; D055848:Nuclear Power Plants; D016273:Occupational Exposure / Q000032:analysis*; D011835:Radiation Protection; D011874:Radiometry / Q000379:methods*; D018570:Risk Assessment; D014481:United States; D014728:Veterans