Bootstrap estimation of diagnostic accuracy with patient-clustered data.
OBJECTIVE
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.


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.


CONCLUSIONS
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.
Rutter
C M
CM
Group Health Cooperative of Puget Sound, Center for Health Studies, Seattle, WA 98101, USA.
D000792:Angiography / Q000592:standards / Q000706:statistics & numerical data*; D019540:Area Under Curve; D001157:Arterial Occlusive Diseases / Q000175:diagnosis*; D016000:Cluster Analysis; D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000592:standards / Q000706:statistics & numerical data*; D012372:ROC Curve