Title: Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors

Abstract

Introduction: Measurement errors are ubiquitous in epidemiological studies, especially for doses arising from environmental and occupational exposures, which are difficult to assess. Correction methods for independent measurement errors, have been comprehensively described elsewhere, including regression calibration, simulation extrapolation (SIMEX), etc. However, when exposure estimates are constructed using complicated physical and biological models, uncertainties in the dosimetry system can become very complex. In such systems, uncertainties in some parameters may affect a large group of study participants simultaneously, and hence are “shared” as opposed to independent dosimetric uncertainties. Representing the structure of the uncertainty in the dosimetry by providing multiple realizations of possible dose has been described for several studies. The goal of this paper is to implement, and explore the properties of, a generalized estimating equation-based approach to shared uncertainties based on principles described in an earlier paper. Methods: The effects of shared and unshared dosimetry error on parameter estimates and confidence intervals in a generalized excess relative risk model are examined. A post-hoc correction method is implemented that constructs corrected confidence intervals based on an underlying variance structure described in an earlier paper. In this correction method it is assumed that the estimated slope parameter in a linear excessmore » relative risk model is distributed as a mixture of normal and lognormal components. A simulation study of the behavior of this correction was developed based on multiple realizations provided by the Monte Carlo dosimetry system used for the plutonium exposures in the Mayak Worker Cohort and a risk model with close similarity to that used in analyses of lung cancer data in that cohort. Results: In the simulation study we found that shared dose errors severely degraded the coverage probabilities of confidence limits for the slope parameter in the linear excess relative risk model relating uncertain exposure to lung cancer risk. Using our correction method to adjust the confidence intervals provided much improved coverage probabilities for this parameter. A smaller amount of degradation in confidence interval coverage for other parameters (e.g. those in the baseline and dose modification terms) was noted, and the proposed correction method also improved coverage for these parameters as well. Discussion: Compared to other recently described methods our proposed correction method relies on relatively familiar software to fit dose response models. The basic modeling philosophy described here first uses existing Poisson regression methods to estimate parameters in the excess relative risk model; in this first stage mean dose (computed from the multiple realizations) is used as the independent variable in the regression. Once the model is fit we then correct (widen) the standard errors for all parameters using the proposed correction method. The simulation studies indicate that the methods described here are an important contribution to the available tools for dealing with shared measurement errors in complex risk analysis.« less

Authors:

Zhang, Zhuo  ^[1];

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• Search DOE PAGES for ORCID "0000-0003-3892-202X"

Preston, Dale L. ^[2]; Sokolnikov, Mikhail ^[3]; Napier, Bruce A.  ^[4];

• Search DOE PAGES for author "Napier, Bruce A."
• Search DOE PAGES for ORCID "0000-0002-7530-4151"

Degteva, Marina ^[5]; Moroz, Brian ^[2]; Vostrotin, Vadim ^[3]; Shiskina, Elena ^[5]; Birchall, Alan ^[6]; Stram, Daniel O. ^[1]; Li, Yun ^[7]

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+ Show Author Affiliations

1. Univ. of Southern California, Los Angeles, CA (United States)
2. Hirosoft International Corp., Eureka, CA (United States)
3. Southern Urals Biophysics Inst., Ozersk (Russia)
4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
5. Urals Research Center for Radiation Medicine, Chelyabinsk (Russia)
6. Global Dosimetry Ltd., Oxon (United Kingdom)
7. Univ. of North Carolina, Chapel Hill, NC (United States)

Publication Date:

Mon Apr 03 00:00:00 EDT 2017

Research Org.:

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

1558388

Report Number(s):

PNNL-SA-122106
Journal ID: ISSN 1932-6203; TRN: US2000193

Grant/Contract Number:  

AC05-76RL01830

Resource Type:

Accepted Manuscript

Journal Name:

PLoS ONE

Additional Journal Information:

Journal Volume: 12; Journal Issue: 4; Journal ID: ISSN 1932-6203

Publisher:

Public Library of Science

Country of Publication:

United States

Language:

English

Subject:

61 RADIATION PROTECTION AND DOSIMETRY; corrected information method; uncertainity analysis; epidemiological analysis; shared error; radiation dose reconstruction