Evaluation of biomarker using two parameter bi-exponential ROC curve

Abstract

Receiver Operating Characteristic (ROC) Curve is used for assessing the ability of a biomarker/screening test to discriminate between non-diseased and diseased subject. In this paper, the parametric ROC curve is studied by assuming two-parameter exponential distribution to the biomarker values. The ROC model developed under this assumption is called bi-exponential ROC (EROC) model. Here, the research interest is to know how far the biomarker will make a distinction between diseased and non-diseased subjects when the gold standard is available using parametric EROC curve and its Area Under the EROC Curve (AUC). Here, the standard error is used as an estimate of the precision of the accuracy measure AUC. The properties of EROC curve that explains the behavior of the EROC curve are also discussed. The AUC along with its asymptotic variance and confidence interval are derived.

Comparison of Item Response Theory Scaling Methods with ROC Analysis

“…Receiver Operating Characteristic (ROC) analysis is a method that may help eliminate these limitations, and performance distributions for further analysis can be obtained with ROC charts (Flach, 2019). It is an analysis used to compare the models or to determine the most accurate estimation method when there are more than one, and to set the criterion value for a situation (Boduroğlu, 2017;Faraggi & Reiser, 2002;Hajian-Tilaki et al, 1997;Hajian-Tilaki, 2018;Heagerty et al, 2000;Jones & Rushton, 2019;Kılıç, 2013;Köksal, 2011;Krzanowski & Hand, 2009;Lasko et al, 2005;Pundir & Amala, 2015;Senaratna, Sooriyarachchim & Meyen, 2015;Swets, Dawes &Monahan, 2000). Roc analysis is based on sensitivity and selectivity.…”

“…Sensitivity (true positive) means that the test puts the individuals who possess a certain quality in this category; whereas selectivity (true negative) means that the test puts the individuals who do not have a certain quality in this category (Flach et al, 2003;Pepe et al, 2004;Pundir & Amala, 2015). Sensitivity and selectivity give the correct classification rate for the case under review (Karaismailoğlu, 2015).…”