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In medical research, continuous markers are widely employed in diagnostictests to distinguish diseased and non-diseased subjects. The accuracy of suchdiagnostic tests is commonly assessed using the receiver operatingcharacteristic (ROC) curve. To summarize an ROC curve and determine its optimalcut-point, the Youden index is popularly used. In literature, estimation of theYouden index has been widely studied via various statistical modelingstrategies on the conditional density. This paper proposes a new model-freeestimation method, which directly estimates the covariate-adjusted cut-pointwithout estimating the conditional density. Consequently, covariate-adjustedYouden index can be estimated based on the estimated cutpoint. The proposedmethod formulates the estimation problem in a large margin classificationframework, which allows flexible modeling of the covariate-adjusted Youdenindex through kernel machines. The advantage of the proposed method isdemonstrated in a variety of simulated experiments as well as a realapplication to Pima Indians diabetes study.

A model-free estimation for the covariate-adjusted Youden index and its associated cut-point