A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data

SUMMARY In this paper we study a broad class of nonparametric statistics for comparing two diagnostic markers. One can compare the sensitivities of these diagnostic markers over restricted ranges of specificity by selecting an appropriate statistic from this class. As special cases, one can compare the entire area under the receiver-operator curve (Hanley & McNeil, 1982), or one can compare the sensitivities at a fixed common specificity. Usually we would recommend a comparison based on an average of sensitivities over a restricted high level of specificities. Test procedures and confidence intervals are based on asymptotic normality. These procedures are applicable for paired data, in which both diagnostic markers are performed on each subject, and for unpaired data. The procedures may be used to compare two real functions of multiple diagnostic markers as well as to compare individual markers.

[1]  Pranab Kumar Sen,et al.  On Some Convergence Properties of UStatistics , 1960 .

[2]  J. Kiefer,et al.  Deviations Between the Sample Quantile Process and the Sample DF , 1985 .

[3]  D. McClish,et al.  Comparing the Areas under More Than Two Independent ROC Curves , 1987, Medical decision making : an international journal of the Society for Medical Decision Making.

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

[5]  S. Greenhouse,et al.  The evaluation of diagnostic tests. , 1950, Biometrics.

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

[7]  David J. Woodruff,et al.  Statistical Inference for , 1951 .

[8]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[9]  John A. Swets,et al.  Evaluation of diagnostic systems : methods from signal detection theory , 1982 .

[10]  R. Pyke,et al.  Weak Convergence of a Two-sample Empirical Process and a New Approach to Chernoff-Savage Theorems , 1968 .

[11]  R. Bast,et al.  A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer. , 1983 .

[12]  K Linnet,et al.  Comparison of quantitative diagnostic tests: type I error, power, and sample size. , 1987, Statistics in medicine.

[13]  P. Major,et al.  An approximation of partial sums of independent RV'-s, and the sample DF. I , 1975 .

[14]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[15]  J. Wellner,et al.  Empirical Processes with Applications to Statistics , 2009 .

[16]  Calyampudi Radhakrishna Rao,et al.  Linear Statistical Inference and its Applications , 1967 .

[17]  V. Zurawski,et al.  Radioimmunometric assay for a monoclonal antibody-defined tumor marker, CA 19-9. , 1983, Clinical chemistry.

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