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

作者：Xu Tu^*; Wang Junhui; Fang Yixin

来源：Statistics in Medicine, 2014, 33(28): 4963-4974.

DOI：10.1002/sim.6290

• diagnostic accuracy
• margin
• receiver operating characteristic curve
• reproducing kernel Hilbert space
• Youden index

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, the 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 cut-point. 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.

• 出版日期2014-12-10