Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study

Oliveira, María; Einbeck, Jochen; Higueras, Manuel; Ainsbury, Elizabeth; Puig, Pedro; Rothkamm, Kai

Abstract

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.

Citation

Oliveira, M., Einbeck, J., Higueras, M., Ainsbury, E., Puig, P., & Rothkamm, K. (2016). Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study. Biometrical Journal, 58(2), 259-279. https://doi.org/10.1002/bimj.201400233

• Journal Article Type: Article
• Acceptance Date: Aug 3, 2015
• Online Publication Date: Oct 7, 2015
• Publication Date: Mar 1, 2016
• Deposit Date: Jan 8, 2016
• Publicly Available Date: Oct 13, 2016
• Journal: Biometrical Journal
• Print ISSN: 0323-3847
• Electronic ISSN: 1521-4036
• Publisher: Wiley-VCH Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 58
• Issue: 2
• Pages: 259-279
• DOI: https://doi.org/10.1002/bimj.201400233
• Keywords: Biological dosimetry, Chromosome aberrations, Count data, Overdispersion, Score tests, Zero inflation.
• Related Public URLs: http://www.ncbi.nlm.nih.gov/pubmed/26461836