Title: Confidence intervals for the area under the receiver operating characteristic curve in the presence of ignorable missing data,
Abstract: Receiver operating characteristic (ROC) curves are widely used as a measure
of accuracy of diagnostic tests and can be summarized using the area under the
ROC curve (AUC). Often, it is useful to construct a confidence intervals for
the AUC, however, since there are a number of different proposed methods to
measure variance of the AUC, there are thus many different resulting methods
for constructing these intervals. In this manuscript, we compare different
methods of constructing Wald-type confidence interval in the presence of
missing data where the missingness mechanism is ignorable. We find that
constructing confidence intervals using multiple imputation (MI) based on
logistic regression (LR) gives the most robust coverage probability and the
choice of CI method is less important. However, when missingness rate is less
severe (e.g. less than 70%), we recommend using Newcombe's Wald method for
constructing confidence intervals along with multiple imputation using
predictive mean matching (PMM).