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Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches

Errington, Adam and Einbeck, Jochen and Cumming, Jonathan (2021) 'Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches.', in Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. Cham: Springer, pp. 393-405. Proceedings of the 2020 UQOP International Conference., 8

Abstract

If individuals are exposed to ionising radiation, due to some radiation accident, for medical reasons, or during spaceflight, there is often a need to estimate the contracted radiation dose. The field of biodosimetry is concerned with estimating the dose retrospectively, using certain biomarkers, which are typically based on counts of some cytogenetic or biomolecular features of the cell arising after radiation-induced double-strand-breaks. Such techniques face particular challenges when the exposure is only partial rather than whole-body, which, when unaccounted for, may lead to grossly inaccurate dose estimates. For biomarkers which are overdispersed, there are currently no procedures available for the detection of partial-body exposures. We consider the question of estimating the exposure fraction as well as quantifying its uncertainty, using Bayesian and frequentist methods, by means of simulation scenarios which are motivated by overdispersed count data (nuclear foci) as arising for the γ −H2AX protein biomarker.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-030-80542-5_24
Publisher statement:This a post-peer-review, pre-copyedit version of a chapter published in Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-80542-5_24
Date accepted:No date available
Date deposited:23 February 2022
Date of first online publication:16 July 2021
Date first made open access:16 July 2022

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