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When evaluating medical tests or biomarkers for disease classification, thearea under the receiver-operating characteristic (ROC) curve is a widely usedperformance metric that does not require us to commit to a specific decisionthreshold. For the same type of evaluations, a different metric known as theaverage precision (AP) is used much more widely in the information retrievalliterature. We study both metrics in some depths in order to elucidate theirdifference and relationship. More specifically, we explain mathematically whythe AP may be more appropriate if the earlier part of the ROC curve is ofinterest. We also address practical matters, deriving an expression for theasymptotic variance of the AP, as well as providing real-world examplesconcerning the evaluation of protein biomarkers for prostate cancer and theassessment of digital versus film mammography for breast cancer screening.

Threshold-free Evaluation of Medical Tests for Classification and Prediction: Average Precision versus Area Under the ROC Curve