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Activity Number:
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33
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #309071 |
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Title:
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Correcting for Shared Measurement Error in Complex Dosimetry Systems
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Author(s):
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Terri K. Johnson*+ and Daniel Stram
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Companies:
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University of Southern California and University of Southern California
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Address:
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1540 Alcazar St CHP220, Los Angeles, CA, 90089,
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Keywords:
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Monte-Carlo maximum likelihood ; full parametric bootstrap ; complex dosimetry ; shared uncertainty ; multiplicative error
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Abstract:
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In occupational cohort studies, a panel of experts often creates an exposure matrix or a dosimetry system that estimates dose histories for workers, and then these estimates are used in disease-risk analysis. Errors in the exposure matrix that were shared by time and/or a group of workers were generally ignored. We tested two different methods (Monte-Carlo maximum likelihood and full parametric bootstrap methods) to study the effect of shared uncertainties. The MCML agreed with the uncorrected likelihood ratio test for small additive and small shared multiplicative error distributions. Clear widening of confidence intervals were seen from the MCML and the full parametric bootstrap methods as the shared multiplicative error increased. Although the confidence intervals widened for both methods under the large error model, the range of the confidence intervals disagreed.
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