Nonparametric ROC Summary Statistics for Correlated Diagnostic Marker Data

TL;DR

This paper introduces efficient nonparametric statistical methods for comparing diagnostic markers and imaging modalities in correlated medical data, improving power over existing techniques.

Contribution

It develops new nonparametric statistics based on weighted ROC area for correlated data, with asymptotic properties and superior power demonstrated through simulations.

Findings

Proposed methods outperform existing statistics in power.

Asymptotic results are derived under complex correlation structures.

Applied successfully to an endometriosis diagnosis study.

Abstract

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. The asymptotic results of the proposed methods are developed under a complex correlation structure. Our simulation studies show that the proposed statistics result in much better powers than existing statistics. We applied the proposed statistics to an endometriosis diagnosis study.