Bootstrap estimation of diagnostic accuracy with patient-clustered data

Rationale and Objectives. The purpose of this study was to describe a simple bootstrap approach for estimating sensitivity, specificity, and the area under the receiver operating characteristic curve for multisite test outcome data. Materials and Methods. The performance of bootstrap estimates was evaluated and compared with that of analytic estimates by using a simulation study. Bootstrapping was demonstrated by using data from a previous study comparing two angiographic methods. Results. Analytic and bootstrap estimates had similar coverage rates for 95% confidence intervals. With many sites per patient, bootstrap estimates had slightly better coverage than analytic estimates. Bootstrap percentile intervals had better coverage than asymptotic normal bootstrap intervals. Conclusion. Bootstrapping is a useful method of estimating confidence intervals for the area under the receiver operating characteristic curve, sensitivity, and specificity when data are correlated.