Guidelines for the Partial Area under the Summary Receiver Operating Characteristic (SROC) Curve

Abstract

<p> The accuracy of a diagnostic test is often evaluated with the measures of sensitivity and specificity and the joint dependence between these two measures is captured by the receiver operating characteristic (ROC) curve. To combine multiple testing results from studies that are assumed to follow the same underlying probability law, a smooth summary receiver operating characteristic (SROC) curve can be fitted. Moses et al. (1993) proposed a least squares approach to fit the smooth SROC curve. </p> <p> In this thesis we overview the summary measures for the ROC curve in single study data as well as the summary statistics for the SROC curves in meta-analysis. These summary statistics include, the area under the curve (AUC), Q* statistic, area swept under the curve (ASC) and the partial area under the curve (pAUC). </p> <p> Our focus, however is mainly on the partial area under the SROC curve as it is being used frequently in meta-analysis of diagnostic testing. The appeal to use the pAUC instead of the full AUC is that the partial area can be used to focus on a clinically relevant region of the SROC curve where false positive rate (FPR) is small. Simulations and considerations for the use of the summary indices of the ROC and SROC curves are presented here. </p>