Methods for estimation of radiation risk in epidemiological studies accounting for classical and Berkson errors in doses
- Publication Type:
- Journal Article
- International Journal of Biostatistics, 2011, 7 (1)
- Issue Date:
Files in This Item:
|[The International Journal of Biostatistics] Methods for Estimation of Radiation Risk in Epidemiological Studies Accounting for Classical and Berkson Errors in Doses.pdf||Published Version||570.08 kB|
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
With a binary response Y, the dose-response model under consideration is logistic in flavor with pr(Y=1 | D) = R (1+R)-1, R = λ0 + EAR D, where λ0 is the baseline incidence rate and EAR is the excess absolute risk per gray. The calculated thyroid dose of a person i is expressed as Dimes= fiQimes/Mimes. Here, Qimesis the measured content of radioiodine in the thyroid gland of person i at time tmes, Mimesis the estimate of the thyroid mass, and fi is the normalizing multiplier. The Qi and Mi are measured with multiplicative errors ViQand ViM, so that Qimes= QitrViQ(this is classical measurement error model) and Mitr = MimesViM(this is Berkson measurement error model). Here, Qitris the true content of radioactivity in the thyroid gland, and Mitris the true value of the thyroid mass. The error in fi is much smaller than the errors in (Qimes, Mimes) and ignored in the analysis. By means of Parametric Full Maximum Likelihood and Regression Calibration (under the assumption that the data set of true doses has lognormal distribution), Nonparametric Full Maximum Likelihood, Nonparametric Regression Calibration, and by properly tuned SIMEX method we study the influence of measurement errors in thyroid dose on the estimates of λ0 and EAR. The simulation study is presented based on a real sample from the epidemiological studies. The doses were reconstructed in the framework of the Ukrainian-American project on the investigation of Post-Chernobyl thyroid cancers in Ukraine, and the underlying subpolulation was artificially enlarged in order to increase the statistical power. The true risk parameters were given by the values to earlier epidemiological studies, and then the binary response was simulated according to the dose-response model. © 2011 Berkeley Electronic Press. All rights reserved.
Please use this identifier to cite or link to this item: