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- 저자 : Pablo Martínez-Camblor (검색결과 2 건)
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General nonparametric ROC curve comparison
Pablo Martínez-Camblor,Carlos Carleos,Norberto Corral 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.1
Although the equality among two or more ROC (receiver operating characteristic) curves is usually contrasted from the respective AUCs (area under the ROC curve), two different ROC curves can share the same AUC and, in order to compare the ROC curves equality,most general criteria must be considered. In this paper, the authors deal with the general ROC curve comparison problem on paired design. They extend the tests for the classical cumulative distribution functions (CDF) comparison to the ROC curves context. To approximate the statistic distribution, two different resampling plans are considered; the usual one based on permutations and a new bootstrap procedure which does not require the exchangeability assumption. As usual, from Monte Carlo simulations, the performance of the proposed methodology is studied for two traditional tests; one based on the Kolmogorov–Smirnov criteria and the other one on the L2-measure. The observed results suggest that the proposed bootstrap provides a good statistic distribution approximation for medium sample size. Therefore the studied methodology allows us to compare the equality of ROC curves by defining a criteria according to the needs of the problem.
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Area under the ROC curve comparison in the presence of missing data
Pablo Martínez-Camblor 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.4
The area under the receiver operating characteristic (ROC) curve (AUC) is broadly acceptedand often used as a diagnostic accuracy index. Moreover, the equality among the predictivecapacity of two or more diagnostic systems is frequently checked from the comparison oftheir respective AUCs. In paired designs, this comparison is usually performed by using onlythe subjects who have collected all the necessary information, in the so-called availablecaseanalysis. On the other hand, the presence of missing data is a frequent problem,especially in retrospective and observational studies. The loss of statistical power andthe misuse of the available information (with the resulting ethical implications) are themain consequences. In this paper a non-parametric method is developed to exploit allavailable 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, sometechnical issues are also reported in the Appendix.
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