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ROC curve analysis for biomarkers based on pooled assessments
Author(s) -
Faraggi David,
Reiser Benjamin,
Schisterman Enrique F.
Publication year - 2003
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1418
Subject(s) - pooling , receiver operating characteristic , statistics , area under the curve , computer science , biomarker , area under curve , mean squared error , medicine , mathematics , artificial intelligence , biology , pharmacokinetics , biochemistry
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. Copyright © 2003 John Wiley & Sons, Ltd.