Accounting for Shared and Unshared Dosimetric Uncertainties in the Dose Response for Ultrasound-Detected Thyroid Nodules after Exposure to Radioactive Fallout

Land, Charles E.; Kwon, Deukwoo; Hoffman, F.O.; Moroz, Brian E.; Drozdovitch, Vladimir; Bouville, André; Beck, Harold L.; Luckyanov, Nickolas; Weinstock, Robert M.; Simon, Steven L.

doi:10.1667/rr13794.1

Cited by 54 publications

(65 citation statements)

References 38 publications

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“…To compare the reliability of this method with conventional regression, we simulated 180 sets of disease incidence of thyroid nodules from prespecified “true” slopes of a linear dose response using 2DMC dose vectors from the dose models used in the study of Land et al . (41). As a metric of reliability, we compared the percentage of test simulations in which the true slope of the dose response was captured by the 95% confidence interval of the estimated slope.…”

Section: Methodsmentioning

confidence: 99%

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The Two-Dimensional Monte Carlo: A New Methodologic Paradigm for Dose Reconstruction for Epidemiological Studies

Simon

Hoffman

Hofer³

2015

Radiation Research

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Retrospective dose estimation, particularly dose reconstruction that supports epidemiological investigations of health risk, relies on various strategies that include models of physical processes and exposure conditions with detail ranging from simple to complex. Quantification of dose uncertainty is an essential component of assessments for health risk studies since, as is well understood, it is impossible to retrospectively determine the true dose for each person. To address uncertainty in dose estimation, numerical simulation tools have become commonplace and there is now an increased understanding about the needs and what is required for models used to estimate cohort doses (in the absence of direct measurement) to evaluate dose response. It now appears that for dose-response algorithms to derive the best, unbiased estimate of health risk, we need to understand the type, magnitude and interrelationships of the uncertainties of model assumptions, parameters and input data used in the associated dose estimation models. Heretofore, uncertainty analysis of dose estimates did not always properly distinguish between categories of errors, e.g., uncertainty that is specific to each subject (i.e., unshared error), and uncertainty of doses from a lack of understanding and knowledge about parameter values that are shared to varying degrees by numbers of subsets of the cohort. While mathematical propagation of errors by Monte Carlo simulation methods has been used for years to estimate the uncertainty of an individual subject’s dose, it was almost always conducted without consideration of dependencies between subjects. In retrospect, these types of simple analyses are not suitable for studies with complex dose models, particularly when important input data are missing or otherwise not available. The dose estimation strategy presented here is a simulation method that corrects the previous deficiencies of analytical or simple Monte Carlo error propagation methods and is termed, due to its capability to maintain separation between shared and unshared errors, the two-dimensional Monte Carlo (2DMC) procedure. Simply put, the 2DMC method simulates alternative, possibly true, sets (or vectors) of doses for an entire cohort rather than a single set that emerges when each individual’s dose is estimated independently from other subjects. Moreover, estimated doses within each simulated vector maintain proper inter-relationships such that the estimated doses for members of a cohort subgroup that share common lifestyle attributes and sources of uncertainty are properly correlated. The 2DMC procedure simulates inter-individual variability of possibly true doses within each dose vector and captures the influence of uncertainty in the values of dosimetric parameters across multiple realizations of possibly true vectors of cohort doses. The primary characteristic of the 2DMC approach, as well as its strength, are defined by the proper separation between uncertainties shared by members of the entire cohort or members of defined cohort su...

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Section: Methodsmentioning

confidence: 99%

“…Finally, Land et al . (41) completed a comprehensive dose reconstruction of thyroid exposures from radioactive fallout and simulated 5,000 cohort realizations using the 2DMC method described here.…”

Section: Introductionmentioning

confidence: 99%

The Two-Dimensional Monte Carlo: A New Methodologic Paradigm for Dose Reconstruction for Epidemiological Studies

Simon

Hoffman

Hofer³

2015

Radiation Research

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“…That is, we do not use the Monte Carlo realizations themselves to fit the model, only to correct the confidence limits of the fitted parameters. Other methods have been proposed for the problem of shared dosimetry error in epidemiological studies [8, 15]. Kwon et al [15] fit the risk model to each realization in turn and then resample realizations with weights dependent on the likelihood of each fit.…”

Section: Discussionmentioning

confidence: 99%

“…To represent these uncertainties, CIDER system provided 100 dose replications for the entire cohort, where both shared and unshared components affect the correlation structure of the replications. Other examples include studies of 131 I exposure in Kazakhstan [8], and studies of occupational and residential exposure to radiation from the Mayak plutonium production plant in the Southern Urals region of Russia. We are particularly motivated by work on dosimetry for the Techa River Cohort study [9–11] and the Mayak Workers Cohort study [3, 12].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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In epidemiological studies, exposures of interest are often measured with uncertainties, which may be independent or correlated. Independent errors can often be characterized relatively easily while correlated measurement errors have shared and hierarchical components that complicate the description of their structure. For some important studies, Monte Carlo dosimetry systems that provide multiple realizations of exposure estimates have been used to represent such complex error structures. While the effects of independent measurement errors on parameter estimation and methods to correct these effects have been studied comprehensively in the epidemiological literature, the literature on the effects of correlated errors, and associated correction methods is much more sparse. In this paper, we implement a novel method that calculates corrected confidence intervals based on the approximate asymptotic distribution of parameter estimates in linear excess relative risk (ERR) models. These models are widely used in survival analysis, particularly in radiation epidemiology. Specifically, for the dose effect estimate of interest (increase in relative risk per unit dose), a mixture distribution consisting of a normal and a lognormal component is applied. This choice of asymptotic approximation guarantees that corrected confidence intervals will always be bounded, a result which does not hold under a normal approximation. A simulation study was conducted to evaluate the proposed method in survival analysis using a realistic ERR model. We used both simulated Monte Carlo dosimetry systems (MCDS) and actual dose histories from the Mayak Worker Dosimetry System 2013, a MCDS for plutonium exposures in the Mayak Worker Cohort. Results show our proposed methods provide much improved coverage probabilities for the dose effect parameter, and noticeable improvements for other model parameters.

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“…The assessment and treatment of uncertainties is a complex area and approaches to this are still developing as knowledge improves. For instance, new approaches have been proposed since the end of CURE to take into account both shared and unshared sources of uncertainties on dose estimates in epidemiological analyses (Land et al 2015, Simon et al 2015, Stram et al 2015, Kwon et al 2016. It was recognised from the start of CURE that, due to the limited time and resources available within the project it would not be possible to develop the definitive final methodology for the treatment of uncertainties as part of future analyses, but also that this should be a priority for future research.…”

Section: Discussionmentioning

confidence: 99%

Concerted Uranium Research in Europe (CURE): toward a collaborative project integrating dosimetry, epidemiology and radiobiology to study the effects of occupational uranium exposure

et al. 2016

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The potential health impacts of chronic exposures to uranium, as they occur in occupational settings, are not well characterized. Most epidemiological studies have been limited by small sample sizes, and a lack of harmonization of methods used to quantify radiation doses resulting from uranium exposure. Experimental studies have shown that uranium has biological effects, but their implications for human health are not clear. New studies that would combine the strengths of large, well-designed epidemiological datasets with those of state-of-the-art biological methods would help improve the characterization of the biological and health effects of occupational uranium exposure. The aim of the European Commission concerted action CURE (Concerted Uranium Research in Europe) was to develop protocols for such a future collaborative research project, in which dosimetry, epidemiology and biology would be integrated to better characterize the effects of occupational uranium exposure. These protocols were developed from existing European cohorts of workers exposed to uranium together with expertise in epidemiology, biology and dosimetry of CURE partner institutions. The preparatory work of CURE should allow a large scale collaborative project to be launched, in order to better characterize the effects of uranium exposure and more generally of alpha particles and low doses of ionizing radiation.

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Accounting for Shared and Unshared Dosimetric Uncertainties in the Dose Response for Ultrasound-Detected Thyroid Nodules after Exposure to Radioactive Fallout

Cited by 54 publications

References 38 publications

The Two-Dimensional Monte Carlo: A New Methodologic Paradigm for Dose Reconstruction for Epidemiological Studies

The Two-Dimensional Monte Carlo: A New Methodologic Paradigm for Dose Reconstruction for Epidemiological Studies

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

Concerted Uranium Research in Europe (CURE): toward a collaborative project integrating dosimetry, epidemiology and radiobiology to study the effects of occupational uranium exposure

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