[en] Several methods for the analysis of occupational radiation exposure data, including procedures based on Cox's proportional hazards model, are presented and evaluated. Issues of interest include the contribution of an external control, the effective handling of highly skewed exposure data, and the potential for detecting effects in populations occupationally exposed to radiation. Expressions for evaluating the power of various procedures are derived and applied to data from the Hanford population in order to determine power curves for detecting leukemia effects, with both additive and multiplicative linear models being used. It is found that the introduction of an external control can increase power, although not when an overall adjustment factor must be estimated from the data or when death rates for the study population are substantially lower than those for the control population. It is also found that very little power is lost if exposures are grouped. Finally, the power calculations indicate, as expected, that in analyses of occupationally exposed populations, such as the Hanford workers, there is very little chance of detecting radiation effects at the levels of our current estimates. However, power is reasonably good for detecting effects that are 10 to 15 times larger

[en] Recent studies indicate that, for finite sample sizes, moment estimators may be superior to maximum likelihood estimators in some regions of parameter space. In this paper a statistic based on the central moment of the sample is expanded in a Taylor series using 24 derivatives and many more terms than previous expansions. A summary algorithm is required to find meaningful approximants using the higher-order coefficients. A example is presented and a comparison between theoretical assessment and simulation results is made

[en] Epidemiolgic studies have strongly suggested that a vast majority (80-90%) of cancers are caused by radiation, chemical and biologic agents; the remainder result from endogenous or genetic factors. Biologically, cancer is most probably the end result of a complex multistage process and therefore may be due to a sequence of exposures to different agents at each of these stages. This emphasizes the need to stress the study of interactions in epidemiologic studies to a greater extent than has been done thus far. Examples of the importance of interactions in several types of cancer are presented

[en] In vitro dose-response curves are used to describe the relation between chromosome aberrations and radiation dose for human lymphocytes. The lymphocytes are exposed to low-LET radiation, and the resulting dicentric chromosome aberrations follow the Poisson distribution. The expected yield depends on both the magnitude and the temporal distribution of the dose. A general dose-response model that describes this relation has been presented by Kellerer and Rossi (1972, Current Topics on Radiation Research Quarterly 8, 85-158; 1978, Radiation Research 75, 471-488) using the theory of dual radiation action. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting dose-time-response models are intrinsically nonlinear in the parameters. A general-purpose maximum likelihood estimation procedure is described, and estimation for the nonlinear models is 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