Oliveira, María and Einbeck, Jochen and Higueras, Manuel and Ainsbury, Elizabeth and Puig, Pedro and Rothkamm, Kai (2016) 'Zero-inflated regression models for radiation-induced chromosome aberration data : a comparative study.', Biometrical journal., 58 (2). pp. 259-279.
Within the field of cytogenetic biodosimetry, Poisson regression is the classical approach for modeling the number of chromosome aberrations as a function of radiation dose. However, it is common to find data that exhibit overdispersion. In practice, the assumption of equidispersion may be violated due to unobserved heterogeneity in the cell population, which will render the variance of observed aberration counts larger than their mean, and/or the frequency of zero counts greater than expected for the Poisson distribution. This phenomenon is observable for both full- and partial-body exposure, but more pronounced for the latter. In this work, different methodologies for analyzing cytogenetic chromosomal aberrations datasets are compared, with special focus on zero-inflated Poisson and zero-inflated negative binomial models. A score test for testing for zero inflation in Poisson regression models under the identity link is also developed.
|Keywords:||Biological dosimetry, Chromosome aberrations, Count data, Overdispersion, Score tests, Zero inflation.|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||http://dx.doi.org/10.1002/bimj.201400233|
|Publisher statement:||This is the accepted version of the following article: Oliveira, María, Einbeck, Jochen, Higueras, Manuel, Ainsbury, Elizabeth, Puig, Pedro and Rothkamm, Kai (2016) Zero-inflated regression models for radiation-induced chromosome aberration data: a comparative study. Biometrical journal, 58(2): 259-279, which has been published in final form at http://dx.doi.org/10.1002/bimj.201400233. This article may be used for non-commercial purposes in accordance With Wiley-VCH Terms and Conditions for self-archiving.|
|Date accepted:||03 August 2015|
|Date deposited:||14 January 2016|
|Date of first online publication:||07 October 2015|
|Date first made open access:||13 October 2016|
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