Roc Curve Analysis for Biomarkers Based on Pooled Assessments

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.

[1]  K. Zou,et al.  Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests. , 1997, Statistics in medicine.

[2]  Robert W. Mee.  Confidence Intervals for Probabilities and Tolerance Regions Based on a Generalization of the Mann-Whitney Statistic , 1990 .

[3]  RJ Chilton.  Recent discoveries in assessment of coronary heart disease: impact of vascular mechanisms on development of atherosclerosis , 2001, The Journal of the American Osteopathic Association.

[4]  Jun S. Liu,et al.  Linear Combinations of Multiple Diagnostic Markers , 1993 .

[5]  B Reiser,et al.  Measuring the effectiveness of diagnostic markers in the presence of measurement error through the use of ROC curves. , 2000, Statistics in medicine.

[6]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[7]  B. Reiser,et al.  Estimation of the area under the ROC curve , 2002, Statistics in medicine.

[8]  B Reiser,et al.  Statistical inference for the area under the receiver operating characteristic curve in the presence of random measurement error. , 2001, American journal of epidemiology.

[9]  Mitchell H. Gail,et al.  A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data , 1989 .

[10]  D Faraggi,et al.  The effect of random measurement error on receiver operating characteristic (ROC) curves. , 2000, Statistics in medicine.

[11]  M. Coffin,et al.  Receiver operating characteristic studies and measurement errors. , 1997, Biometrics.

[12]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[13]  D. Bamber.  The area above the ordinal dominance graph and the area below the receiver operating characteristic graph , 1975 .

[14]  B. Reiser,et al.  Confidence intervals for the generalized ROC criterion. , 1997, Biometrics.

[15]  P. Robinson.  The interpretation of diagnostic tests. , 1987, Nuclear medicine communications.

[16]  S. Humphries,et al.  Inflammation, obesity, stress and coronary heart disease: is interleukin-6 the link? , 2000, Atherosclerosis.

[17]  Siu-Keung Tse,et al.  Estimation of p(y , 1986 .

[18]  I Hinberg,et al.  Receiver operator characteristic (ROC) curves and non-normal data: an empirical study. , 1990, Statistics in medicine.

[19]  Irwin Guttman,et al.  Statistical inference for Pr(Y < X): The normal case , 1986 .

[20]  Marcello Pagano,et al.  On the informativeness and accuracy of pooled testing in estimating prevalence of a rare disease: Application to HIV screening , 1995 .

[21]  C R Weinberg,et al.  Using Pooled Exposure Assessment to Improve Efficiency in Case‐Control Studies , 1999, Biometrics.

[22]  A Parametric Approach to Measurement Errors in Receiver Operating Characteristic Studies , 1996 .

[23]  C. Farrington.  Estimating prevalence by group testing using generalized linear models. , 1992, Statistics in medicine.

[24]  John I. McCool,et al.  Inference on p{y , 1991 .

[25]  W. Klitz,et al.  Association mapping of disease loci, by use of a pooled DNA genomic screen. , 1997, American journal of human genetics.