Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 2515-2527 |
| Number of pages | 13 |
| Journal | Statistics in Medicine |
| Volume | 22 |
| Issue number | 15 |
| DOIs | |
| State | Published - Aug 15 2003 |
| Externally published | Yes |
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Keywords
- Area under the ROC
- Gamma distribution
- Normal distribution
- Root mean square error
ASJC Scopus subject areas
- Epidemiology
Cite this
ROC curve analysis for biomarkers based on pooled assessments. / Faraggi, David; Reiser, Benjamin; Schisterman, Enrique F.
In: Statistics in Medicine, Vol. 22, No. 15, 15.08.2003, p. 2515-2527.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - ROC curve analysis for biomarkers based on pooled assessments
AU - Faraggi, David
AU - Reiser, Benjamin
AU - Schisterman, Enrique F.
PY - 2003/8/15
Y1 - 2003/8/15
N2 - 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.
AB - 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.
KW - Area under the ROC
KW - Gamma distribution
KW - Normal distribution
KW - Root mean square error
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UR - http://www.scopus.com/inward/citedby.url?scp=0043136517&partnerID=8YFLogxK
U2 - 10.1002/sim.1418
DO - 10.1002/sim.1418
M3 - Article
C2 - 12872306
AN - SCOPUS:0043136517
VL - 22
SP - 2515
EP - 2527
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 15
ER -