Bootstrap confidence intervals for the sensitivity of a quantitative diagnostic test

We examine bootstrap approaches to the analysis of the sensitivity of quantitative diagnostic test data. Methods exist for inference concerning the sensitivity of one or more tests for fixed levels of specificity, taking into account the variability in the sensitivity due to variability in the test values for normal subjects. However, parametric methods do not adequately account for error, particularly when the data are non-normally distributed, and non-parametric methods have low power. We implement bootstrap methods for confidence limits for the sensitivity of a test for a fixed specificity and demonstrate that under certain circumstances the bootstrap method gives more accurate confidence intervals than do other methods, while it performs at least as well as other methods in many standard situations.