15 research outputs found
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Statistical Analysis of Data with Non-Detectable Values
Environmental exposure measurements are, in general, positive and may be subject to left censoring, i.e. the measured value is less than a ''limit of detection''. In occupational monitoring, strategies for assessing workplace exposures typically focus on the mean exposure level or the probability that any measurement exceeds a limit. A basic problem of interest in environmental risk assessment is to determine if the mean concentration of an analyte is less than a prescribed action level. Parametric methods, used to determine acceptable levels of exposure, are often based on a two parameter lognormal distribution. The mean exposure level and/or an upper percentile (e.g. the 95th percentile) are used to characterize exposure levels, and upper confidence limits are needed to describe the uncertainty in these estimates. In certain situations it is of interest to estimate the probability of observing a future (or ''missed'') value of a lognormal variable. Statistical methods for random samples (without non-detects) from the lognormal distribution are well known for each of these situations. In this report, methods for estimating these quantities based on the maximum likelihood method for randomly left censored lognormal data are described and graphical methods are used to evaluate the lognormal assumption. If the lognormal model is in doubt and an alternative distribution for the exposure profile of a similar exposure group is not available, then nonparametric methods for left censored data are used. The mean exposure level, along with the upper confidence limit, is obtained using the product limit estimate, and the upper confidence limit on the 95th percentile (i.e. the upper tolerance limit) is obtained using a nonparametric approach. All of these methods are well known but computational complexity has limited their use in routine data analysis with left censored data. The recent development of the R environment for statistical data analysis and graphics has greatly enhanced the availability of high quality nonproprietary (open source) software that serves as the basis for implementing the methods in this paper
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Maximum likelihood estimation for cytogenetic dose-response curves
In vitro dose-response curves are used to describe the relation between the yield of dicentric chromosome aberrations and radiation dose for human lymphocytes. The dicentric yields follow the Poisson distribution, and the expected yield depends on both the magnitude and the temporal distribution of the dose for low LET radiation. A general dose-response model that describes this relation has been obtained by Kellerer and Rossi using the theory of dual radiation action. The yield of elementary lesions is kappa(..gamma..d + g(t, tau)d/sup 2/), where t is the time and d is dose. The coefficient of the d/sup 2/ term is determined by the recovery function and the temporal mode of irradiation. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting models are intrinsically nonlinear in the parameters. A general purpose maximum likelihood estimation procedure is described and illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure
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Statistical issues in radiation dose-response analysis of employees of the nuclear industry in Oak Ridge, Tennessee
Poisson regression methods are used to describe dose-response relations for cancer mortality for a subcohort of 28,347 white male radiation workers. Age specific baseline rates are described using both internal and external (US white male) rates. Regression analyses are based on an analytic data structure (ADS) that consists of a table of observed deaths, expected deaths, and person-years at risk for each combination of levels of seven risk factors. The factors are socioeconomic status, length of employment, birth cohort, age at risk, facility, internal exposure, and external exposure. Each observation in the ADS consists of the index value of each of the stratifying factors, the observed deaths, the expected deaths, the person-years, and the ten year lagged average cumulative dose. Regression diagnostics show that a linear exponential relative risk model is not appropriate for these data. Results are presented using a main effects model for factors other than external radiation, and an excess relative risk term for cumulative external radiation dose
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Adjusting external doses from the ORNL and Y-12 facilities for the Oak Ridge Nuclear Facilities mortality study
This report presents specific procedures used for adjusting radiation doses to radiation personnel at the ORNL and Y-12 plants during the early years. Topics discussed include: background information; selection of employment years to be considered; hardcopy monitoring methods and records; pocket meter data; and replacement of 1943 unmonitored employment years. These topics were discussed for both years