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A model‐free estimation for the covariate‐adjusted Youden index and its associated cut‐point
Author(s) -
Xu Tu,
Wang Junhui,
Fang Yixin
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6290
Subject(s) - youden's j statistic , covariate , receiver operating characteristic , statistics , kernel density estimation , index (typography) , point (geometry) , mathematics , kernel (algebra) , margin (machine learning) , computer science , machine learning , geometry , combinatorics , estimator , world wide web
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. Copyright © 2014 John Wiley & Sons, Ltd.