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Receiver operating characteristic (ROC) curves are widely used as a measureof accuracy of diagnostic tests and can be summarized using the area under theROC curve (AUC). Often, it is useful to construct a confidence intervals forthe AUC, however, since there are a number of different proposed methods tomeasure variance of the AUC, there are thus many different resulting methodsfor constructing these intervals. In this manuscript, we compare differentmethods of constructing Wald-type confidence interval in the presence ofmissing data where the missingness mechanism is ignorable. We find thatconstructing confidence intervals using multiple imputation (MI) based onlogistic regression (LR) gives the most robust coverage probability and thechoice of CI method is less important. However, when missingness rate is lesssevere (e.g. less than 70%), we recommend using Newcombe's Wald method forconstructing confidence intervals along with multiple imputation usingpredictive mean matching (PMM).

Confidence intervals for the area under the receiver operating characteristic curve in the presence of ignorable missing data