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2016 ; 17
(3
): 422-36
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Estimation of radiation risk in presence of classical additive and Berkson
multiplicative errors in exposure doses
#MMPMID26795191
Masiuk SV
; Shklyar SV
; Kukush AG
; Carroll RJ
; Kovgan LN
; Likhtarov IA
Biostatistics
2016[Jul]; 17
(3
): 422-36
PMID26795191
show ga
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.
|*Chernobyl Nuclear Accident
[MESH]
|*Dose-Response Relationship, Radiation
[MESH]
|*Models, Theoretical
[MESH]
|Computer Simulation
[MESH]
|Humans
[MESH]
|Radiation Exposure/*statistics & numerical data
[MESH]