On linear combinations of biomarkers to improve diagnostic accuracy

Aiyi Liu, Enrique F. Schisterman, Yan Zhu

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)37-47
Number of pages11
JournalStatistics in Medicine
Volume24
Issue number1
DOIs
StatePublished - Jan 15 2005
Externally publishedYes

Keywords

  • Dominance of an ROC curve
  • Maximizing sensitivity
  • Receiver operating characteristic (ROC) curve
  • Sensitivity and specificity

ASJC Scopus subject areas

  • Epidemiology

Fingerprint Dive into the research topics of 'On linear combinations of biomarkers to improve diagnostic accuracy'. Together they form a unique fingerprint.

  • Cite this