Adjusting the generalized ROC curve for covariates

Enrique F. Schisterman, David Faraggi, Benjamin Reiser

Research output: Contribution to journalArticlepeer-review


Receiver operating characteristic (ROC) curves and in particular the area under the curve (AUC), are widely used to examine the effectiveness of diagnostic markers. Diagnostic markers and their corresponding ROC curves can be strongly influenced by covariate variables. When several diagnostic markers are available, they can be combined by a best linear combination such that the area under the ROC curve of the combination is maximized among all possible linear combinations. In this paper we discuss covariate effects on this linear combination assuming that the multiple markers, possibly transformed, follow a multivariate normal distribution. The ROC curve of this linear combination when markers are adjusted for covariates is estimated and approximate confidence intervals for the corresponding AUC are derived. An example of two biomarkers of coronary heart disease for which covariate information on age and gender is available is used to illustrate this methodology.

Original languageEnglish
Pages (from-to)3319-3331
Number of pages13
JournalStatistics in Medicine
Issue number21
StatePublished - 15 Nov 2004


  • Best linear combination
  • Box-Cox transformations
  • Diagnostic markers
  • Sensitivity
  • Specificity

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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