Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data
International Statistical Review
Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because 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 article, 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 based on logistic regression gives the most robust coverage probability and the choice of confidence interval 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.
Cho, Hunyong; Matthews, Gregory J.; and Harel, Ofer. Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data. International Statistical Review, 87, 1: 152-177, 2019. Retrieved from Loyola eCommons, Mathematics and Statistics: Faculty Publications and Other Works, http://dx.doi.org/10.1111/insr.12277
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© The Authors, 2019.
Author Posting © The Authors, 2019. This is the author's version of the work. It is posted here by permission of The Authors for personal use, not for redistribution. The definitive version was published in International Statistical Review, Volume 87, Issue 1, April 2019. https://doi.org/10.1111/insr.12277