Abstract Number:
2959
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Mark Little (1), Nobuyuki Hamada (2), Lydia Zablotska (3)
Institutions:
(1) Radiation Epidemiology Branch, National Cancer Institute, N/A, (2) Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, CRIEPI, Chiba, Japan, (3) UCSF, N/A
Co-Author(s):
Nobuyuki Hamada
Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, CRIEPI
First Author:
Mark Little
Radiation Epidemiology Branch, National Cancer Institute
Presenting Author:
Mark Little
Radiation Epidemiology Branch, National Cancer Institute
Abstract Text:
Low-dose radiation risks must be extrapolated from groups exposed at much higher levels of dose. Recently, there has been much attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings.
We assess regression calibration (RC), Monte Carlo maximum likelihood (MCML), frequentist model averaging (FMA), 2D Monte Carlo+Bayesian model averaging (2DMC+BMA) methods with simulated datasets having mixed Berkson and classical errors. We test all these against a modification of RC, the extended regression calibration (ERC) method. 2DMC+BMA has poor coverage for the dose coefficients; coverage of RC, MCML, FMA is better, although uniformly too high for FMA and MCML, and best for ERC. Bias in predicted relative risk is generally smallest for ERC, and largest for quasi-2DMC+BMA and FMA methods, with RC and MCML exhibiting bias in predicted risk somewhat intermediate between the other two methods. In summary, the new ERC method performs well in comparison to previously proposed methods and is particularly suited to situations with low to moderate amounts of shared and unshared Berkson errors.
Keywords:
Covariate measurement error|Berkson error|Classical error|Regression calibration|Bayesian model averaging|Frequentist model averaging
Sponsors:
Section on Statistics and the Environment
Tracks:
Environmental Policy and Regulations
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