On linear combinations of biomarkers to improve diagnostic accuracy.
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.
Liu
Aiyi
A
Division 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
Schisterman
Enrique F
EF
Zhu
Yan
Y
D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000032:analysis* / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood; D003327:Coronary Disease / Q000097:blood / Q000175:diagnosis; D005260:Female; D006801:Humans; D016014:Linear Models; D014975:Lutein / Q000097:blood; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012680:Sensitivity and Specificity; D017392:Thiobarbituric Acid Reactive Substances / Q000032:analysis; D014527:Uric Acid / Q000097:blood
D015415:Biomarkers; D008076:Cholesterol, HDL; D017392:Thiobarbituric Acid Reactive Substances; D014527:Uric Acid; D014975:Lutein