Two-way partial AUC and its properties

Simultaneous control on true positive rate and false positive rate is of significant importance in the performance evaluation of diagnostic tests. Most of the established literature utilizes partial area under receiver operating characteristic (ROC) curve with restrictions only on false positive rate (FPR), called FPR pAUC, as a performance measure. However, its indirect control on true positive rate (TPR) is conceptually and practically misleading. In this paper, a novel and intuitive performance measure, named as two-way pAUC, is proposed, which directly quantifies partial area under ROC curve with explicit restrictions on both TPR and FPR. To estimate two-way pAUC, we devise a nonparametric estimator. Based on the estimator, a bootstrap-assisted testing method for two-way pAUC comparison is established. Moreover, to evaluate possible covariate effects on two-way pAUC, a regression analysis framework is constructed. Asymptotic normalities of the methods are provided. Advantages of the proposed methods are illustrated by simulation and Wisconsin Breast Cancer Data. We encode the methods as a publicly available R package tpAUC.

[1]  Cynthia Rudin,et al.  The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List , 2009, J. Mach. Learn. Res..

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

[3]  Lori E. Dodd,et al.  Partial AUC Estimation and Regression , 2003, Biometrics.

[4]  Margaret S. Pepe,et al.  Receiver Operating Characteristic Methodology , 2000 .

[5]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[6]  Xiao-Hua Zhou,et al.  Statistical Methods in Diagnostic Medicine , 2002 .

[7]  Margaret S. Pepe,et al.  A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing , 1997 .

[8]  Lori E. Dodd,et al.  Semiparametric Regression for the Area Under the Receiver Operating Characteristic Curve , 2003 .

[9]  David Gur,et al.  On use of partial area under the ROC curve for evaluation of diagnostic performance , 2013, Statistics in medicine.

[10]  Gang Li,et al.  A Unified Approach to Nonparametric Comparison of Receiver Operating Characteristic Curves for Longitudinal and Clustered Data , 2008, Journal of the American Statistical Association.

[11]  A. Dwyer,et al.  In pursuit of a piece of the ROC. , 1996, Radiology.

[12]  N A Obuchowski,et al.  Nonparametric analysis of clustered ROC curve data. , 1997, Biometrics.

[13]  Robert V. Foutz,et al.  On the Unique Consistent Solution to the Likelihood Equations , 1977 .

[14]  Hua Liu ASYMPTOTIC PROPERTIES OF PARTIAL AREAS UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE WITH APPLICATIONS IN MICROARRAY EXPERIMENTS , 2006 .

[15]  Jean L Freeman,et al.  A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets. , 2002, Statistics in medicine.

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

[17]  D. McClish Analyzing a Portion of the ROC Curve , 1989, Medical decision making : an international journal of the Society for Medical Decision Making.

[18]  B. Turnbull,et al.  NONPARAMETRIC AND SEMIPARAMETRIC ESTIMATION OF THE RECEIVER OPERATING CHARACTERISTIC CURVE , 1996 .

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

[20]  H. H. Song,et al.  Analysis of correlated ROC areas in diagnostic testing. , 1997, Biometrics.

[21]  Stuart G. Baker,et al.  A Proposed Design and Analysis for Comparing Digital and Analog Mammography , 2001 .

[22]  C. Metz,et al.  A receiver operating characteristic partial area index for highly sensitive diagnostic tests. , 1996, Radiology.

[23]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[24]  Michael Escobar,et al.  Nonparametric statistical inference method for partial areas under receiver operating characteristic curves, with application to genomic studies , 2008, Statistics in medicine.

[25]  David Gur,et al.  Jackknife variance of the partial area under the empirical receiver operating characteristic curve , 2017, Statistical methods in medical research.

[26]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[27]  Yichuan Zhao,et al.  A nonparametric approach for partial areas under ROC curves and ordinal dominance curves , 2017 .

[28]  S D Walter,et al.  The partial area under the summary ROC curve , 2005, Statistics in medicine.

[29]  Zhanfeng Wang,et al.  Marker selection via maximizing the partial area under the ROC curve of linear risk scores. , 2011, Biostatistics.

[30]  Dan Roth,et al.  Generalization Bounds for the Area Under the ROC Curve , 2005, J. Mach. Learn. Res..

[31]  Haibo Zhou,et al.  Estimation of AUC or Partial AUC Under Test-Result-Dependent Sampling , 2012, Statistics in biopharmaceutical research.