How to account for uncertainty due to measurement errors in an uncertainty analysis using Monte Carlo simulation.
Two kinds of error are considered, namely Berkson and classical measurement error. The true values of the measurands will never be known. Possibly true sets of values are generated by the Monte Carlo simulation of the uncertainty analysis. This is straightforward for Berkson errors but requires the modeling of statistical dependence between measured values and errors in the classical case. A method is presented that enables this dependence modeling as part of the uncertainty analysis. Practical examples demonstrate the applicability of the method. Two "quick fixes" are also discussed together with their shortcomings. The uncertainty analysis of the application of a small computer model from the area of dose reconstruction illustrates, by example, the effect both kinds of error can have on model results like individual dose values and mean value and standard deviation of the population dose distribution.
Hofer
Eduard
E
elhofer@t-online.de
D000465:Algorithms; D000818:Animals; D001822:Body Burden; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D011786:Quality Control; D011829:Radiation Dosage; D011874:Radiometry / Q000379:methods*; D012062:Relative Biological Effectiveness; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity