ROC curve analysis for biomarkers based on pooled assessments

David Faraggi, Benjamin Reiser, Enrique F. Schisterman

Research output: Contribution to journalArticlepeer-review


Interleukin-6 is a biomarker of inflammation which has been suggested as having potential discriminatory ability for myocardial infarction. Because of its high assaying cost it is very expensive to evaluate this marker. In order to reduce this cost we propose pooling the specimens. In this paper we examine the efficiency of ROC curve analysis, specifically the estimation of the area under the ROC curve, when dealing with pooled data. We study the effect of pooling when there are only a fixed number of individuals available for testing and pooling is carried out to save on the number of assays. Alternatively we examine how many pooled assays of size g are necessary to provide essentially the same information as N individual assays. We measure loss of information by means of the change in root mean square error of the estimate of the area under the ROC curve and study the extent of this loss via a simulation study.

Original languageEnglish
Pages (from-to)2515-2527
Number of pages13
JournalStatistics in Medicine
Issue number15
StatePublished - 15 Aug 2003


  • Area under the ROC
  • Gamma distribution
  • Normal distribution
  • Root mean square error

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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