Estimation of radiation risk in presence of classical additive and Berkson multiplicative errors in exposure doses
| Autor: | Illya Likhtarov, Sergii Masiuk, Alexander Kukush, Raymond J. Carroll, Kovgan Ln, Sergiy Shklyar |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Regression calibration Poison control Risk Assessment 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Statistics Humans Computer Simulation 0101 mathematics Simulation Mathematics Estimation Observational error Multiplicative function Dose-Response Relationship Radiation Regression analysis Articles General Medicine Models Theoretical Radiation Exposure Berkson error model Chernobyl Nuclear Accident 030220 oncology & carcinogenesis Relative risk Statistics Probability and Uncertainty |
| Zdroj: | Biostatistics. 17:422-436 |
| ISSN: | 1468-4357 1465-4644 |
| DOI: | 10.1093/biostatistics/kxv052 |
| Popis: | In this paper, the influence of measurement errors in exposure doses in a regression model with binary response is studied. Recently, it has been recognized that uncertainty in exposure dose is characterized by errors of two types: classical additive errors and Berkson multiplicative errors. The combination of classical additive and Berkson multiplicative errors has not been considered in the literature previously. In a simulation study based on data from radio-epidemiological research of thyroid cancer in Ukraine caused by the Chornobyl accident, it is shown that ignoring measurement errors in doses leads to overestimation of background prevalence and underestimation of excess relative risk. In the work, several methods to reduce these biases are proposed. They are new regression calibration, an additive version of efficient SIMEX, and novel corrected score methods. |
| Databáze: | OpenAIRE |
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