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Shared dosimetry error in epidemiological dose-response analyses

Journal Article · · PLoS ONE
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10]
  1. Univ. of Southern California, Los Angeles, CA (United States)
  2. Hirosoft International, Eureka, CA (United States)
  3. Southern Urals Biophysics Institute, Ozersk (Russia)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  5. Fred Hutchinson Cancer Center, Seattle, WA (United States)
  6. Vanderbilt Univ., Nashville, TN (United States)
  7. U.S. Dept. of Energy, New York, NY (United States)
  8. Risk Assessment Corporation, Neeses, SC (United States)
  9. National Cancer Institute, Rockville, MD (United States)
  10. Leibniz Institute for Prevention Research and Epidemiology (Germany)
+ Show Author Affiliations
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.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
HS0000091; SC0008944
OSTI ID:
1193633
Alternate ID(s):
OSTI ID: 1244817
Journal Information:
PLoS ONE, Journal Name: PLoS ONE Journal Issue: 3 Vol. 10; ISSN 1932-6203
Publisher:
Public Library of ScienceCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (7)

Assessment of thyroid cancer risk associated with radiation dose from personal diagnostic examinations in a cohort study of US radiologic technologists, followed 1983–2014 journal May 2018
Impact of Uncertainties in Exposure Assessment on Thyroid Cancer Risk among Persons in Belarus Exposed as Children or Adolescents Due to the Chernobyl Accident journal October 2015
Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors journal April 2017
Dosimetry and uncertainty approaches for the million person study of low-dose radiation health effects: overview of the recommendations in NCRP Report No. 178 journal November 2018
The Million Person Study, whence it came and why journal April 2019
Implications of recent epidemiologic studies for the linear nonthreshold model and radiation protection journal August 2018
Shared and unshared exposure measurement error in occupational cohort studies and their effects on statistical inference in proportional hazards models journal February 2018

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61 RADIATION PROTECTION AND DOSIMETRY