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 unsharedmore »
- Authors:
-
- Univ. of Southern California, Los Angeles, CA (United States)
- Hirosoft International, Eureka, CA (United States)
- Southern Urals Biophysics Institute, Ozersk (Russia)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Fred Hutchinson Cancer Center, Seattle, WA (United States)
- Vanderbilt Univ., Nashville, TN (United States)
- U.S. Dept. of Energy, New York, NY (United States)
- Risk Assessment Corporation, Neeses, SC (United States)
- National Cancer Institute, Rockville, MD (United States)
- Leibniz Institute for Prevention Research and Epidemiology (Germany)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1193633
- Grant/Contract Number:
- HS0000091; SC0008944
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 3; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 61 RADIATION PROTECTION AND DOSIMETRY
Citation Formats
Stram, Daniel O., Preston, Dale L., Sokolnikov, Mikhail, Napier, Bruce, Kopecky, Kenneth J., Boice, John, Beck, Harold, Till, John, Bouville, Andre, and Zeeb, Hajo. Shared dosimetry error in epidemiological dose-response analyses. United States: N. p., 2015.
Web. doi:10.1371/journal.pone.0119418.
Stram, Daniel O., Preston, Dale L., Sokolnikov, Mikhail, Napier, Bruce, Kopecky, Kenneth J., Boice, John, Beck, Harold, Till, John, Bouville, Andre, & Zeeb, Hajo. Shared dosimetry error in epidemiological dose-response analyses. United States. https://doi.org/10.1371/journal.pone.0119418
Stram, Daniel O., Preston, Dale L., Sokolnikov, Mikhail, Napier, Bruce, Kopecky, Kenneth J., Boice, John, Beck, Harold, Till, John, Bouville, Andre, and Zeeb, Hajo. Mon .
"Shared dosimetry error in epidemiological dose-response analyses". United States. https://doi.org/10.1371/journal.pone.0119418. https://www.osti.gov/servlets/purl/1193633.
@article{osti_1193633,
title = {Shared dosimetry error in epidemiological dose-response analyses},
author = {Stram, Daniel O. and Preston, Dale L. and Sokolnikov, Mikhail and Napier, Bruce and Kopecky, Kenneth J. and Boice, John and Beck, Harold and Till, John and Bouville, Andre and Zeeb, Hajo},
abstractNote = {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.},
doi = {10.1371/journal.pone.0119418},
journal = {PLoS ONE},
number = 3,
volume = 10,
place = {United States},
year = {Mon Mar 23 00:00:00 EDT 2015},
month = {Mon Mar 23 00:00:00 EDT 2015}
}
Web of Science
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