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Abstract
BackgroundIn medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. Combining the measurements of these biomarkers into one single score is a popular practice to integrate the collected information, where the accuracy of the resultant diagnostic test is usually improved. To measure the accuracy of a diagnostic test, the Youden index has been widely used in literature. Various parametric and nonparametric methods have been proposed to linearly combine biomarkers so that the corresponding Youden index can be optimized. Yet there seems to be little justification of enforcing such a linear combination.MethodsThis paper proposes a flexible approach that allows both linear and nonlinear combinations of biomarkers. The proposed approach formulates the problem in a large margin classification framework, where the combination function is embedded in a flexible reproducing kernel Hilbert space.ResultsAdvantages of the proposed approach are demonstrated in a variety of simulated experiments as well as a real application to a liver disorder study.ConclusionLinear combination of multiple diagnostic biomarkers are widely used without proper justification. Additional research on flexible framework allowing both linear and nonlinear combinations is in need.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-015-0085-z) contains supplementary material, which is available to authorized users.
Highlights
In medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests
The receiver operating characteristic (ROC) curve is defined as sensitivity versus 1−specificity over all possible cut-points for a given biomarker [2, 3], which is a comprehensive plot that displays the influence of a biomarker as the cut-point varies
The Youden index has been successfully applied in many clinical studies and served as an appropriate summary for the diagnostic accuracy of a single quantitative measurement (e.g., [2, 6, 7]). It has been widely accepted by medical researchers that diagnosis based on one single biomarker may not provide sufficient accuracy [8, 9]. It is becoming more and more common that multiple biomarker tests are performed on each individual, and the corresponding measurements are combined into one single score to help clinicians make better diagnostic judgment
Summary
It is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. The Youden index, defined as the maximum vertical distance between the ROC curve and the 45◦ line, is an It has been widely accepted by medical researchers that diagnosis based on one single biomarker may not provide sufficient accuracy [8, 9]. It is becoming more and more common that multiple biomarker tests are performed on each individual, and the corresponding measurements are combined into one single score to help clinicians make better diagnostic judgment. Pepe and Thompson [11] proposed to relax the distributional assumption and perform a grid search
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