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In medical research, it is common to collect information of multiplecontinuous biomarkers to improve the accuracy of diagnostic tests. Combiningthe measurements of these biomarkers into one single score is a popularpractice to integrate the collected information, where the accuracy of theresultant diagnostic test is usually improved. To measure the accuracy of adiagnostic test, the Youden index has been widely used in literature. Variousparametric and nonparametric methods have been proposed to linearly combinebiomarkers so that the corresponding Youden index can be optimized. Yet thereseems to be little justification of enforcing such a linear combination. Thispaper proposes a flexible approach that allows both linear and nonlinearcombinations of biomarkers. The proposed approach formulates the problem in alarge margin classification framework, where the combination function isembedded in a flexible reproducing kernel Hilbert space. Advantages of theproposed approach are demonstrated in a variety of simulated experiments aswell as a real application to a liver disorder study.

Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy