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Updated: Aug 21, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
On linear combinations of biomarkers to improve diagnostic accuracy
Aiyi Liu1, Enrique F Schisterman, Yan Zhu
1Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, Department of Health and Human Services, 6100 Executive Blvd., Rockville, MD 20852, USA. Liua@mail.nih.gov
Abstract:
We consider combining multiple biomarkers to improve diagnostic accuracy. Su and Liu derived the linear combinations that maximize the area under the receiver operating characteristic (ROC) curves. These linear combinations, however, may have unsatisfactory low sensitivity over a certain range of desired specificity. In this paper, we consider maximizing sensitivity over a range of specificity. We first present a simpler proof for Su and Liu's main theorem and further investigate some other optimal properties of their linear combinations. We then derive alternative linear combinations that have higher sensitivity over a range of high (or low) specificity. The methods are illustrated using data from a study evaluating biomarkers for coronary heart disease.
