Area under the ROC curve comparison in the presence of missing data

• Published in: Journal of the Korean Statistical Society Vol. 42; no. 4; pp. 431 - 442
• Main Author: Martínez-Camblor, Pablo
• Format: Journal Article
• Language: English
• Published: Singapore Elsevier B.V 01.12.2013; Springer Singapore; 한국통계학회
• Subjects: Applied Statistics; AUC; Bayesian Inference; Bootstrap method; Missing data; Non-parametric test; ROC curve; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; 통계학; secondary; Missing data; Non-parametric test; Bootstrap method; ROC curve; primary; AUC; secondary 62P10; primary 62G10
• ISSN: 1226-3192; 2005-2863
• DOI: 10.1016/j.jkss.2013.01.004

The area under the receiver operating characteristic (ROC) curve (AUC) is broadly accepted and often used as a diagnostic accuracy index. Moreover, the equality among the predictive capacity of two or more diagnostic systems is frequently checked from the comparison of their respective AUCs. In paired designs, this comparison is usually performed by using only the subjects who have collected all the necessary information, in the so-called available-case analysis. On the other hand, the presence of missing data is a frequent problem, especially in retrospective and observational studies. The loss of statistical power and the misuse of the available information (with the resulting ethical implications) are the main consequences. In this paper a non-parametric method is developed to exploit all available information. In order to approximate the distribution for the proposed statistic, the asymptotic distribution is computed and two different resampling plans are studied. In addition, the methodology is applied to a real-world medical problem. Finally, some technical issues are also reported in the Appendix.