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CORRECTING FOR SHARED MEASUREMENT ERROR
IN COMPLEX DOSIMETRY SYSTEMS
by
Terri Kang Johnson
------------------------------------------------------------------------------------------------------
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOSTATISTICS)
May 2007
Copyright 2007
Terri Kang Johnson
Object Description
| Title | Correcting for shared measurement error in complex dosimetry systems |
| Author | Johnson, Terri Kang |
| Author email | tkang@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Biostatistics |
| School | Keck School of Medicine |
| Date defended/completed | 2007-03-22 |
| Date submitted | 2007 |
| Restricted until | Unrestricted |
| Date published | 2007-06-07 |
| Advisor (committee chair) | Stram, Daniel O. |
| Advisor (committee member) |
Thomas, Duncan Langholz, Bryan Gilliland, Frank Tavaré, Simon |
| Abstract | 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 have developed and tested two different methods (Monte-Carlo maximum likelihood and fully parametric bootstrap methods) to study the effect of shared uncertainties. A simple simulation experiment showed that the MCML agreed with the uncorrected likelihood ratio test for small additive and small shared multiplicative error distributions. Clear widening of confidence intervals was seen from the MCML and the fully 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. Hence, a validation analysis was conducted using the Oak Ridge National Laboratory cohort data. We performed multiple runs of the MCML method on newly created outcome data from running the fully parametric bootstrap method, and saw that the MCML method was quite a feasible way to correct for shared uncertainties. However, the results from the MCML method applied to the Colorado Uranium Miners data showed additional work may be necessary. The results suggest that 1,000 replications created from the complex dosimetry system of the Colorado Uranium Miners may not be sufficient to fully capture the variability of uncertainties. In addition, true dose may need to be sampled from a distribution conditional on both disease and input data in a case when there is a strong speculation that dose-response relationship exists.; These comparisons between Stram and Kopecky's SUMA method, the MCML, and the fully parametric bootstrap method will give guidance to future use of "complex dosimetry systems." |
| Keyword | complex dosimetry system; monte carlo maximum likelihood; parametric bootstrap; shared error; radiation risk; Colorado Plateau Uranium Miners; Oak Ridge National Laboratory |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m519 |
| Rights | Johnson, Terri Kang |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Johnson-20070607 |
| Archival file | uscthesesreloadpub_Volume62/etd-Johnson-20070607.pdf |
Description
| Title | Page 1 |
| Full text | CORRECTING FOR SHARED MEASUREMENT ERROR IN COMPLEX DOSIMETRY SYSTEMS by Terri Kang Johnson ------------------------------------------------------------------------------------------------------ A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOSTATISTICS) May 2007 Copyright 2007 Terri Kang Johnson |
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