An Additive Selection of Markers to Improve Diagnostic Accuracy Based on a Discriminatory Measure

Abstract

Rationale and Objectives The estimation of the area under the receiver operating characteristic (ROC) curve (AUC) often relies on the assumption that the truly positive population tends to have higher marker results than the truly negative population. The authors propose a discriminatory measure to relax such an assumption and apply the measure to identify the appropriate set of markers for combination. Materials and Methods The proposed measure is based on the maximum of the AUC and 1-AUC. The existing methods are applied to estimate the measure. The subset of markers are selected using a combination method which maximizes a function of the proposed discriminatory score with the number of markers as a penalty in the function. Results The properties of the estimators for the proposed measure were studied through large-scale simulation studies. The application was illustrated through a real example to identify the set of markers to combine. Conclusion Simulation results showed excellent small-sample performance of the estimators for the proposed measure. The application in the example yielded a reasonable subset of markers for combination.

Optimal linear combinations of multiple diagnostic biomarkers based on Youden index

“…Furthermore, Tang et al proposed to maximize a discriminatory measure based on AUC to accommodate situations when a random true diseased subject does not necessarily have higher marker value than a random healthy subject. The discriminatory measure is defined as i = max( AUC ,1 − AUC ).…”