title: A Monte Carlo maximum likelihood method for estimating uncertainty arising from shared errors in exposures in epidemiological studies of nuclear workers.
abstract: Errors in the estimation of exposures or doses are a major source of uncertainty in epidemiological studies of cancer among nuclear workers. This paper presents a Monte Carlo maximum likelihood method that can be used for estimating a confidence interval that reflects both statistical sampling error and uncertainty in the measurement of exposures. The method is illustrated by application to an analysis of all cancer (excluding leukemia) mortality in a study of nuclear workers at the Oak Ridge National Laboratory (ORNL). Monte Carlo methods were used to generate 10,000 data sets with a simulated corrected dose estimate for each member of the cohort based on the estimated distribution of errors in doses. A Cox proportional hazards model was applied to each of these simulated data sets. A partial likelihood, averaged over all of the simulations, was generated; the central risk estimate and confidence interval were estimated from this partial likelihood. The conventional unsimulated analysis of the ORNL study yielded an excess relative risk (ERR) of 5.38 per Sv (90% confidence interval 0.54-12.58). The Monte Carlo maximum likelihood method yielded a slightly lower ERR (4.82 per Sv) and wider confidence interval (0.41-13.31).
authors: Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois, Chicago, Illinois 60612-4394, USA. lstayner@uic.edu
authors: Leslie
authors: L
authors: Stayner
authors: Martine
authors: M
authors: Vrijheid
authors: Elisabeth
authors: E
authors: Cardis
authors: Daniel O
authors: DO
authors: Stram
authors: Isabelle
authors: I
authors: Deltour
authors: Stephen J
authors: SJ
authors: Gilbert
authors: Geoffrey
authors: G
authors: Howe
mesh_terms: D009010:Monte Carlo Method; D016273:Occupational Exposure / Q000706:statistics & numerical data*; D011834:Radiation Monitoring / Q000706:statistics & numerical data*; D012107:Research Design; D013997:Time Factors; D035501:Uncertainty