Bootstrap resampling: a powerful method of assessing confidence intervals for doses from experimental data

Bootstrap resampling provides a versatile and reliable statistical method f or estimating the accuracy of quantities which are calculated from experime ntal data. It is an empirically based method, in which large numbers of sim ulated datasets are generated by computer from existing measurements, so th at approximate confidence intervals of the derived quantities may be obtain ed by direct numerical evaluation. A simple introduction to the method is g iven via a detailed example of estimating 95% confidence intervals for cumu lated activity in the thyroid following injection of Tc-99m-sodium pertechn etate using activity-time data from 23 subjects. The application of the app roach to estimating confidence limits for the self-dose to the kidney follo wing injection of Tc-99m-DTPA organ imaging agent based on uptake data from 19 subjects is also illustrated. Results are then given for estimates of d oses to the foetus following administration of Tc-99m-sodium pertechnetate for clinical reasons during pregnancy, averaged over 25 subjects. The boots trap method is well suited for applications in radiation dosimetry includin g uncertainty, reliability and sensitivity analysis of dose coefficients in biokinetic models, but it can also be applied in a wide range of other bio medical situations.