Radiation Risk Estimation

The De Gruyter Series in Mathematics and Life Sciences is devoted to the publication of monographs in the field. They cover topics and methods in fields of current interest that use mathematical approaches to understand and explain, model and influence phenomena in all areas of life sciences. This includes, among others, theory and application of biological mathematical modeling, complex systems biology, bioinformatics, computational biomodeling stochastic modeling, biostatistics, computational evolutionary biology, comparative genomics, or structural bioinformatics. Also, new types of mathematical problems that arise from biological knowledge shall be covered.

This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies. Contents: Part I - Estimation in regression models with errors in covariatesMeasurement error modelsLinear models with classical errorPolynomial regression with known variance of classical errorNonlinear and generalized linear models Part II Radiation risk estimation under uncertainty in exposure dosesOverview of risk models realized in program package EPICUREEstimation of radiation risk under classical or Berkson multiplicative error in exposure dosesRadiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accidentElements of estimating equations theoryConsistency of efficient methodsEfficient SIMEX method as a combination of the SIMEX method and the corrected score methodApplication of regression calibration in the model with additive error in exposure doses

Auteur

S. Masiuk, A. Kukush, S. Shklyar, M. Chepurny, I. Likhtarov, Radiation Protection Institute, Ukraine.

Résumé
"[...] this monograph is highly recommended."
Xia Wang in: Mathematical Reviews Clippings (2018), MR3726857