Estimation of radiation risk in presence of classical additive and Berkson multiplicative errors in exposure doses.

In this paper, the influence of measurement errors in exposure doses in a regression model with binary response is studied. Recently, it has been recognized that uncertainty in exposure dose is characterized by errors of two types: classical additive errors and Berkson multiplicative errors. The combination of classical additive and Berkson multiplicative errors has not been considered in the literature previously. In a simulation study based on data from radio-epidemiological research of thyroid cancer in Ukraine caused by the Chornobyl accident, it is shown that ignoring measurement errors in doses leads to overestimation of background prevalence and underestimation of excess relative risk. In the work, several methods to reduce these biases are proposed. They are new regression calibration, an additive version of efficient SIMEX, and novel corrected score methods.

Berkson errors in radiation dose assessments and their impact on radiation risk estimates.

OBJECTIVE: To estimate the influence of Berkson errors in exposure doses on the results of risk analysis within example of radiation epidemiological studies of the thyroid cancer prevalence. MATERIALS AND METHODS: The impact of Berkson errors of the thyroid doses in a dose-response analysis is studied by the method of stochastic simulation. RESULTS: Presence of errors in doses results in bias of the naive estimations of baseline morbidity and absolute risk excess in the linear logistic regression model. With the increase of dose errors the bias of the naive estimate increases almost linearly. The use of the full maximum likelihood method developed by authors improves essentially the estimates of parameters in the excess absolute risk model. CONCLUSION: Ignoring of the significant Berkson errors in dose assessment leads to the bias in estimates of background morbidity (overestimated) and of the excess absolute risk (underestimated). At that the bias is essentially less than the one for the case of classical error.