Keywords: minimum detectable dose, expectation maximisation algorithm, unbiased characteristic estimate, log-normal dose distribution, epidemiological data analysis, low radiation, epidemiology, dose-response relationship
Improvement of epidemiological data analysis by unbiased estimates of log-normal dose distribution
The effects of radiation on population and risk assessment are studied through epidemiological studies that are heavily dependent on dose distribution. Radiation doses to individual workers follow a log-normal distribution. Computation of the collective dose with the minimum detectable values (Detection Limit or DL) as zero is biased. Hence the expectation value of the 'missed' doses is computed as the ratio of the collective dose of all dosimeters with DL to the product of the total number of dosimeters. So far, in all epidemiological studies, doses below DL are set equal to the DL itself and yield a positive bias in the dose-response relation. This paper presents an enhanced method by removing the bias with the help of the Expectation Maximisation (EM) algorithm. This algorithm, along with unbiased characteristic estimates of log-normal distribution, significantly improves the estimation of confounders by 95%, and improves the dose-response relationship.