Power and Uncertainty Analysis of Epidemiologic Studies of Radiation-Related Disease Risk in which Dose Estimates are Based on a Complex Dosimetry System: Some Observations.
Daniel O. Stram, Department of Preventive Medicine, School of Medicine, University of Southern California, Los Angeles, California.
This paper discusses practical effects of dosimetry error relevant to the design and analysis of an epidemiologic study of disease risk and exposure. It focuses on shared error in radiation-dose estimates for such studies as the Hanford Thyroid Disease Study or the Utah Thyroid Cohort Study, which use a complex dosimetry system that produces multiple replications of possible dose for the cohort. I argue that a simple estimation of shared multiplicative error components via direct examination of the replications of dose for each person provides information useful for estimating the power of a study to detect a radiation effect. Uncertainty analysis (construction of confidence intervals) can be approached in the same way in simple cases. I also offer some suggestions for Monte Carlo-based confidence intervals.
Several recent epidemiologic studies have used a complex dosimetry to estimate radiation-related health effects of exposures to fallout or nuclear-plant releases. Two examples are the Utah Thyroid Cohort Study   and the Hanford Thyroid Disease Study (Draft Report). In both, individual doses of radioactive iodine (131I) to the thyroid gland were estimated decades after exposure. In this report, I make a number of observations concerning analysis of study power to detect a simple dose-response relationship and analysis of the uncertainty in estimated dose-response relationships. Those issues involve the uncertainty of dose itself in the study. The setting differs from the traditional measurement-error problem in that errors in dosimetry are not independent from subject to subject. For example, in the 131I setting, the basic approach (cf ) is to estimate total deposition of 131I onto grass, uptake by cows on pasture, transfer of iodine into cow’s milk, milk consumption by children, and uptake to the thyroid gland. For each subject, i, in the study, a set of personal data, Wi, regarding age and location of residence during the exposure period, source (backyard cow vs local dairy) and amount of milk consumed, and so on, is collected. The dosimetry system uses those data to impute a dose estimate. Shared uncertainties result, obviously, if such quantities as the total deposition onto grass or the average fraction of 131I that is excreted into cow’s milk are misidentified. Other uncertainties can be regarded as independent from person to person (for example, differences between true and reported milk consumption).
Increasingly, the approach to uncertainty in dose in such studies seems to be to develop a dosimetry system that gives not just one estimate of dose, but rather many replications (100 in the case of the Hanford study) of possible dose for each subject. Moreover, the estimates are not generated independently for each subject; instead, each run of the dosimetry system provides a new realization of possible dose for the entire study population, and the uncertainties in shared