“School of Mathematics”
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| Paper IPM / M / 17638 |
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| Abstract: | |
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The area under a receiver operating characteristic (ROC) curve is frequently used in
medical studies to evaluate the effectiveness of a continuous diagnostic biomarker,
with values closer to one indicating better classification. Unfortunately, the standard
statistical procedures based on simple random sampling (SRS) and ranked set sam-
pling (RSS) techniques tend to be less efficient when the values of the area under a
ROC curve (AUC) get closer to one. Thus, developing some statistical procedures
for efficiently estimating the AUC when it is close to one is very important. In this
paper, some estimators are developed using nomination sampling to assess AUC.
The proposed AUC estimators are compared with their counterparts in SRS and
RSS using Monte Carlo simulation. The results show that some of the estimators
developed in this study considerably improve the efficiency of the AUC estimation
when it is close to one. This substantially reduces the cost and time for the sample
size needed to obtain the desired precision.
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