Search NASA⌕ Search

Engineering topics

Stram, Daniel O.

Publications and source records attributed to Stram, Daniel O..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Dosimetry and uncertainty approaches for the million person study of low-dose radiation health effects: overview of the recommendations in NCRP Report No. 178

Purpose: Scientific Committee 6–9 was established by the National Council on Radiation Protection and Measurements (NCRP), charged to provide guidance in the derivation of organ doses and their uncertainty, and produced a report, NCRP Report No. 178, Deriving Organ Doses and their Uncertainty for Epidemiologic Studies with a focus on the Million Person Study of Low-Dose Radiation Health Effects (MPS). This review summarizes the conclusions and recommendations of NCRP Report No. 178, with a concentration on and overview of the dosimetry and uncertainty approaches for the cohorts in the MPS, along with guidelines regarding the essential approaches used to estimate organ doses and their uncertainties (from external and internal sources) within the framework of an epidemiologic study. Conclusions: The success of the MPS is tied to the validity of the dose reconstruction approaches to provide realistic estimates of organ-specific radiation absorbed doses that are as accurate and precise as possible and to properly evaluate their accompanying uncertainties. Further, the dosimetry aspects for the MPS are challenging in that they address diverse exposure scenarios for diverse occupational groups being studied over a period of up to 70 y. Specific dosimetric reconstruction issues differ among the varied exposed populations that are considered: atomic veterans, U.S. Department of Energy workers exposed to both penetrating radiation and intakes of radionuclides, nuclear power plant workers, medical radiation workers, and industrial radiographers. While a major source of radiation exposure to the study population comes from external gamma- or x-ray sources, for some of the study groups, there is also a meaningful component of radionuclide intakes that requires internal radiation dosimetry assessments.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

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

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 excess 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.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Shared Dosimetry Error in Epidemiological Dose-Response Analyses

Radiation dose reconstruction systems for large-scale epidemiological studies are sophisticated both in providing estimates of dose and in representing dosimetry uncertainty. For example, a computer program was used by the Hanford Thyroid Disease Study to provide 100 realizations of possible dose to study participants. The variation in realizations reflected the range of possible dose for each cohort member consistent with the data on dose determinates in the cohort. Another example is the Mayak Worker Dosimetry System 2013 which estimates both external and internal exposures and provides multiple realizations of "possible" dose history to workers given dose determinants. This paper takes up the problem of dealing with complex dosimetry systems that provide multiple realizations of dose in an epidemiologic analysis. In this paper we derive expected scores and the information matrix for a model used widely in radiation epidemiology, namely the linear excess relative risk (ERR) model that allows for a linear dose response (risk in relation to radiation) and distinguishes between modifiers of background rates and of the excess risk due to exposure. We show that treating the mean dose for each individual (calculated by averaging over the realizations) as if it was true dose (ignoring both shared and unshared dosimetry errors) gives asymptotically unbiased estimates (i.e. the score has expectation zero) and valid tests of the null hypothesis that the ERR slope β is zero. Although the score is unbiased the information matrix (and hence the standard errors of the estimate of β) is biased for β≠0 when ignoring errors in dose estimates, and we show how to adjust the information matrix to remove this bias, using the multiple realizations of dose. Use of these methods for several studies, including the Mayak Worker Cohort and the U.S. Atomic Veterans Study, is discussed.

epidemiological analysis↗
DOE OSTI2015

Dose Reconstruction for the Million Worker Study: Status and Guidelines

The primary aim of the epidemiologic study of one million U.S. radiation workers and veterans (the Million-Worker study) is to provide scientifically valid information on the level of radiation risk when exposures are received gradually over time, and not acutely as was the case for Japanese atomic bomb survivors. The primary outcome of the epidemiological study is cancer mortality but other causes of death such as cardiovascular disease and cerebrovascular disease will be evaluated. The success of the study is tied to the validity of the dose reconstruction approaches to provide unbiased estimates of organ-specific radiation absorbed doses and their accompanying uncertainties. The dosimetry aspects for the Million-Worker study are challenging in that they address diverse exposure scenarios for diverse occupational groups being studied over a period of up to 70 years. The dosimetric issues differ among the varied exposed populations that are considered: atomic veterans, DOE workers exposed to both penetrating radiation and intakes of radionuclides, nuclear power plant workers, medical radiation workers, and industrial radiographers. While a major source of radiation exposure to the study population comes from external gamma-ray or x-ray sources, for certain of the study groups there is a meaningful component of radionuclide intakes that require internal radiation dosimetry measures. Scientific Committee 6-9 has been established by NCRP to produce a report on the comprehensive organ dose assessment (including uncertainty analysis) for the Million-Worker study. The Committee’s report will cover the specifics of practical dose reconstruction for the ongoing epidemiologic studies with uncertainty analysis discussions and will be a specific application of the guidance provided in NCRP Reports 158, 163, 164, and 171. The main role of the Committee is to provide guidelines to the various groups of dosimetrists involved in the various components of the Million-Worker study to make sure that certain dosimetry criteria are respected: calculation of annual absorbed doses in the organs of interest, separation of low-LET and high-LET components, evaluation of uncertainties, and quality assurance and quality control. Lastly, we recognize that the Million-Worker study and its approaches to dosimetry are a work in progress and that there will be flexibility and changes in direction as new information is obtained, both with regard to dosimetry and with regard to the epidemiologic features of the study components.

59 BASIC BIOLOGICAL SCIENCES↗