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Masiuk SV, Shklyar SV, Kukush AG, et al. Estimation of radiation risk in presence of classical additive and Berkson multiplicative errors in exposure doses. Biostatistics. 2016;17(3):422-36doi: 10.1093/biostatistics/kxv052.
Masiuk, S. V., Shklyar, S. V., Kukush, A. G., Carroll, R. J., Kovgan, L. N., & Likhtarov, I. A. (2016). Estimation of radiation risk in presence of classical additive and Berkson multiplicative errors in exposure doses. Biostatistics (Oxford, England), 17(3), 422-36. https://doi.org/10.1093/biostatistics/kxv052
Masiuk, S V, et al. "Estimation of radiation risk in presence of classical additive and Berkson multiplicative errors in exposure doses." Biostatistics (Oxford, England) vol. 17,3 (2016): 422-36. doi: https://doi.org/10.1093/biostatistics/kxv052
Masiuk SV, Shklyar SV, Kukush AG, Carroll RJ, Kovgan LN, Likhtarov IA. Estimation of radiation risk in presence of classical additive and Berkson multiplicative errors in exposure doses. Biostatistics. 2016 Jul;17(3):422-36. doi: 10.1093/biostatistics/kxv052. Epub 2016 Jan 20. PMID: 26795191; PMCID: PMC4915607.
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Biostatistics. 2016 Jul;17(3):422-36. doi: 10.1093/biostatistics/kxv052. Epub 2016 Jan 20.

Estimation of radiation risk in presence of classical additive and Berkson multiplicative errors in exposure doses.

Biostatistics (Oxford, England)

S V Masiuk, S V Shklyar, A G Kukush, R J Carroll, L N Kovgan, I A Likhtarov

Affiliations

  1. State Institution "National Research Center for Radiation Medicine of the National Academy of Medical Sciences of Ukraine", Melnykova str., 53, Kyiv, 04050, Ukraine; Ukrainian Radiation Protection Institute, Melnykova str., 53, Kyiv, 04050, Ukraine [email protected].
  2. Taras Shevchenko National University of Kyiv, Volodymyrska Str. 64, Kyiv 01601, Ukraine.
  3. Texas A&M University, College Station, TX 77843, USA.
  4. State Institution "National Research Center for Radiation Medicine of the National Academy of Medical Sciences of Ukraine", Melnykova str., 53, Kyiv, 04050, Ukraine; Ukrainian Radiation Protection Institute, Melnykova str., 53, Kyiv, 04050, Ukraine.

PMID: 26795191 PMCID: PMC4915607 DOI: 10.1093/biostatistics/kxv052

Abstract

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.

© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].

Keywords: Berkson measurement error; Chornobyl; Classical measurement error; Corrected scores; Dose-response; Radiation epidemiology; Regression calibration; SIMEX

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