Impact of doses uncertainties on the radiation risks estimation

«Radiation and Risk», 2008, vol. 17, no. 3, pp.64-75

Authors

Masyuk S.V. - Researcher of the Laboratory for Modeling Doses and Radiation Risks of the Department of Radiological Protection. Institute of Radiation Protection of ATS of Ukraine, Kiev.
Shklyar S.V. - Researcher of the Laboratory of Statistical Methods of the Department of Radiological Protection. Institute of Radiation Protection of ATS of Ukraine, Kiev.
Kukush A.G. - Doctor of Physics and Mathematics, Professor of the Department of Mathematical Analysis of the Faculty of Mechanics and Mathematics. T.Shevchenko Kiev National University, Kiev.
Vavilov S.E. - Ph.D., Head of the Laboratory of Statistical Methods of the Department of Radiological Protection. Institute of Radiation Protection of ATS of Ukraine, Kiev.

Abstract

The models of Classical and Berkson’s errors existed together in the exposure doses are considered. The simulation-stochastic modeling of radiation risk at different uncertainty levels in the thyroid radiation doses are developed based on the imitation of real subpopulation of children and teenagers younger 18 years (301907 persons from 1293 settlements of Zhytomyr, Kiev and Chernigiv oblasts of Ukraine). Parameter estimations of absolute risk are taken by “naive” and “non-naive” methods. It is demonstrated the influence of classical and Berkson multiplicative errors variances in the radiation doses on the estimations of absolute risk excess and background rate. The dependences of “naive” and “non-naive” estimations confidence intervals on the level of uncertainties in the radiation doses are analyzed for different types of errors.

Key words
Radiation dose; absolute risk; Berkson's error; classical error; “naive” estimation; confidence interval.

References

1. Bronstein I.N., Semendyaev K.A. A handbook on mathematics for engineers and students of technical colleges. Moscow, Science Publ., 1986. 544 p.

2. Kelton V.D., Low A.M. Simulation modeling. St. Petersburg, Peter Publ., 2004. 847 p.

3. Kleinen, J., Statistical Methods in Simulation Modeling: In 2 volumes, Moscow, Statistics Publ., 1978. 221 pp., 335 p.

4. Korolyuk V.S., Portenko N.I., Skorokhod A.V., Turbin A.F. A handbook on probability theory and mathematical statistics. Moscow, Nauka Publ., 1985. 640 p.

5. Carroll R.J., Ruppert D., Stefanski L.A. Measurement Error in Nonlinear Models. London: Chapman & Hall, 1995. 305 p.

6. Jacob P., Bogdanova T., Chepurniy M. et al. Thyroid cancer risk in areas of Ukraine and Belarus affected by the Chernobyl accident. Rad. Res. 2006. No 165. P. 1-8.

7. Likhtarov I., Kovgan L., Vavilov S., Chepurny M., Bouville A., Luckyanov N., Jacob P., Voilleque P., Voigt G. Post-Chornobyl thyroid cancers in Ukraine. Report 1: Estimation of thyroid doses. Rad. Res. 2005. Vol. 163. PP. 125-136.

8. Likhtarov I., Kovgan L., Vavilov S. et al. Post-Chornobyl thyroid cancers in Ukraine. Report 2. Risk analysis. /Rad. Res. 2006. Vol. 166. PP. 375-386.

9. Ron E., Hoffman F.O. Uncertainties in Radiation dosimetry and their impact on Dose-Response analyses: Proc. of a workshop held September 3-5, 1997 in Bethesda, Maryland. NIH Publication No. 99-4541, 1999. 311 p.

10. Stefansky L.A., Carrol R.J. Conditional Scores and Optimal Scores for Generalized Linear Measurement Error Models. Biometrika. 1987. Vol.74, No. 4. PP. 703-716.

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