  In medical research, continuous markers are widely employed in diagnostic
tests to distinguish diseased and non-diseased subjects. The accuracy of such
diagnostic tests is commonly assessed using the receiver operating
characteristic (ROC) curve. To summarize an ROC curve and determine its optimal
cut-point, the Youden index is popularly used. In literature, estimation of the
Youden index has been widely studied via various statistical modeling
strategies on the conditional density. This paper proposes a new model-free
estimation method, which directly estimates the covariate-adjusted cut-point
without estimating the conditional density. Consequently, covariate-adjusted
Youden index can be estimated based on the estimated cutpoint. The proposed
method formulates the estimation problem in a large margin classification
framework, which allows flexible modeling of the covariate-adjusted Youden
index through kernel machines. The advantage of the proposed method is
demonstrated in a variety of simulated experiments as well as a real
application to Pima Indians diabetes study.
