Radiation Risk Estimation: Based on Measurement Error Models (de Gruyter Series in Mathematics and Life Sciences)

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 covariates** Measurement error models Linear models with classical error Polynomial regression with known variance of classical error Nonlinear and generalized linear models **Part II Radiation risk estimation under uncertainty in exposure doses** Overview of risk models realized in program package EPICURE Estimation of radiation risk under classical or Berkson multiplicative error in exposure doses Radiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accident Elements of estimating equations theory Consistency of efficient methods Efficient SIMEX method as a combination of the SIMEX method and the corrected score method Application of regression calibration in the model with additive error in exposure doses 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 covariates
Measurement error models
Linear models with classical error
Polynomial regression with known variance of classical error
Nonlinear and generalized linear models

Part II Radiation risk estimation under uncertainty in exposure doses
Overview of risk models realized in program package EPICURE
Estimation of radiation risk under classical or Berkson multiplicative error in exposure doses
Radiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accident
Elements of estimating equations theory
Consistency of efficient methods
Efficient SIMEX method as a combination of the SIMEX method and the corrected score method
Application of regression calibration in the model with additive error in exposure doses

List of authors Editor’s Foreword Preface In memoriam Illya Likhtarov (1935–2017) Contents List of symbols, abbreviations, units, and terms Part I. Estimation in regression models with errors in covariates 1. Measurement error models 2. Linear models with classical error 3. Polynomial regression with known variance of classical error 4. Nonlinear and generalized linear models Part II. Radiation risk estimation under uncertainty in exposure doses 5. Overview of risk models realized in program package EPICURE 6. Estimation of radiation risk under classical or Berkson multiplicative error in exposure doses 7. Radiation risk estimation for persons exposed by radioiodine as a result of the Chornobyl accident A Elements of estimating equations theory B. Consistency of efficient methods C. Efficient SIMEX method as a combination of the SIMEX method and the corrected score method D. Application of regression calibration in the model with additive error in exposure doses Bibliography Index Also of Interest 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 mathema