A smaller amount of degradation in confidence interval coverage for other parameters (e.g. Using our correction method to adjust the confidence intervals provided much improved coverage probabilities for this parameter. 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. 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. 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 post-hoc correction method is implemented that constructs corrected confidence intervals based on an underlying variance structure 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. 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. Representing the structure of the uncertainty in the dosimetry by providing multiple realizations more » of possible dose has been described for several studies. 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. However, when exposure estimates are constructed using complicated physical and biological models, uncertainties in the dosimetry system can become very complex. Correction methods for independent measurement errors, have been comprehensively described elsewhere, including regression calibration, simulation extrapolation (SIMEX), etc. Introduction: Measurement errors are ubiquitous in epidemiological studies, especially for doses arising from environmental and occupational exposures, which are difficult to assess. Finally, the accuracy of the program has been thoroughly evaluated in terms of coverage probabilities for a wide range of parameter values. An option has been added which makes it easy for the user to generate tables of confidence limits. The present paper introduces a more efficient computational algorithm, and documents the program for prospective users. An unpublished Fortran program to compute confidence limits directly from sample estimates of p and u2 has been available from the second author, and has been used by a number of investigators to analyze lognormal data sets. If a known transformation of a random variable X is normally distributed with mean (Land, 1975), but their use is often tedious, requiring repeated interpolation and calculation.
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