A Two-Dimensional Monte Carlo Approach to Radiation Dose Reconstruction for Epidemiologic Studies

• Activity Number: 303
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Teaching of Statistics in the Health Sciences
• Author(s): Steven Simon and Owen Hoffman and Eduard Hofer and Ruth Pfeiffer*
• Companies: National Cancer Institute and Oak Ridge Center for Risk Analysis and Retired and National Cancer Institute
• Keywords: uncertainty ; epidemiology ; Monte Carlo ; exposure assessment ; shared errors

Abstract

Dose reconstruction supporting epidemiological investigations rely on various strategies of exposure assessment ranging from simple to complex. Quantification of dose uncertainty is essential since it is impossible to retrospectively determine the true dose for each subject. Heretofore, uncertainty analyses rarely distinguished between errors shared between subjects and those which are not shared. The dose estimation strategy presented here is a simulation method that corrects the previous deficiencies and is termed, due to its capability to maintain separation between shared and unshared errors, the two-dimensional Monte Carlo (2DMC) procedure. In simple terms, the 2DMC method simulates alternative, possibly true sets (or vectors) of doses for an entire cohort rather than a single set with independently assessed doses. Moreover, doses for subjects within any subgroup that share common exposure attributes and sources of uncertainty maintain proper inter-correlations. The 2DMC simulates inter-individual variability of possibly true doses within each dose vector and captures the influence of uncertainty in the dosimetric parameters across dose vectors.