Review of bayesian statistical analysis methods for cytogenetic radiation biodosimetry, with a practical example

Elizabeth Ainsbury*, Volodymyr A. Vinnikov, Pedro Puig, Manuel Higueras, Nataliya A. Maznyk, David C. Lloyd, Kai Rothkamm

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

16 Citations (Scopus)

Abstract

Classical methods of assessing the uncertainty associated with radiation doses estimated using cytogenetic techniques are now extremely well defined. However, several authors have suggested that a Bayesian approach to uncertainty estimation may be more suitable for cytogenetic data, which are inherently stochastic in nature. The Bayesian analysis framework focuses on identification of probability distributions (for yield of aberrations or estimated dose), which also means that uncertainty is an intrinsic part of the analysis, rather than an 'afterthought'. In this paper Bayesian, as well as some more advanced classical, data analysis methods for radiation cytogenetics are reviewed that have been proposed in the literature. A practical overview of Bayesian cytogenetic dose estimation is also presented, with worked examples from the literature.

Original languageEnglish
Pages (from-to)185-196
Number of pages12
JournalRadiation Protection Dosimetry
Volume162
Issue number3
DOIs
Publication statusPublished - 1 Dec 2014

Bibliographical note

Funding Information:
This work was funded by the UK Royal Society, under project number JP080153, and the National Institute for Health Research. The work carried out at UAB was funded by the grant MTM2012-31118. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Publisher Copyright:
© The Author 2013. Published by Oxford University Press. All rights reserved.

Fingerprint

Dive into the research topics of 'Review of bayesian statistical analysis methods for cytogenetic radiation biodosimetry, with a practical example'. Together they form a unique fingerprint.

Cite this