Nonparametric ROC summary statistics for correlated diagnostic marker data.
We propose efficient nonparametric statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve, which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. We develop the asymptotic results of the proposed methods under a complex correlation structure. Our simulation studies show that the proposed statistics result in much better powers than existing statistics. We apply the proposed statistics to an endometriosis diagnosis study.
Department of Statistics, George Mason University, Fairfax, VA 22030, USA. ltang1@gmu.edu
Liansheng Larry
LL
Tang
Aiyi
A
Liu
Zhen
Z
Chen
Enrique F
EF
Schisterman
Bo
B
Zhang
Zhuang
Z
Miao
D019540:Area Under Curve; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods*; D004715:Endometriosis / Q000175:diagnosis; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D012372:ROC Curve; D018709:Statistics, Nonparametric