Risk Estimation in Epidemiological Studies Accounting for Complex Uncertainty in Radiation Dosimetry

• Activity Number: 86
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Deukwoo Kwon*+
• Companies: University of Miami
• Keywords: risk estimation ; dosimetry uncertainty ; radiation exposure

Abstract

In most radiation epidemiologic studies a set of dose estimates is used to assess radiation risk. Mean doses are commonly used. With this practice dosimetry uncertainty in the analysis is not able to be incorporated. There are several attempts to deal with complicated dosimetry which considered either a mixture of Berkson and classical measurement errors or shared errors (Ballick et al. 2002; Li et al. 2007; Stayner et al. 2007). The two dimension-Monte Carlo (2D-MC) proposed by Simon and Hoffman enables us to get quite complicated and realistic dosimetry system. We obtain multiple set of dose realizations (e.g., 1,000) from the 2D-MC. Given multiple dose series we use a Bayesian method to incorporate dosimetry uncertainty in the risk estimation. In our presentation we create three simulation settings using Kazakhstan study dosimetry data and generate thyroid nodule outcomes based on the model used in Charles et al. 2008. We compare results of our method with those of traditional practice with mean doses.