Receiver operating characteristic (ROC) curve for medical researchers

Abstract

Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented.

Fitness Age Score and the Risk of Long-Term Care Insurance Certification—The Kyoto-Kameoka Longitudinal Study

“…The cutoff value (CV) for the FAS score was created with 3 methods: 1) the Youden Index (YI) [13] [14], which evaluates the value for which "sensitivity -(1 -specificity)" is the highest; 2) a method that evaluates the shortest distance from the coordinates (false-positive ratio, sensitivity) = (0, 1) to the ROC curve (the Minimum Distance method [MD]) [15]; and 3) the Two-Graph ROC method (TG) [16], which determines the value that is at the intersection of the FAS score-sensitivity curve and the FAS score-specificity curve.…”

Comparison of the Eosinophil Count to C –reactive protein, Leukocyte Count, and Neutrophil Count for the detection of bacterial infection in ill-appearing children with fever admitted to the Emergency Department

“…The best cut-off value was chosen using the Youden's index (the difference between the true positive rate and the false positive rate). (16) Maximizing this index allows to find, from the ROC curve, an optimal cut-off point independently from the prevalence. Multivariate analysis using logistic regression was performed to identify the independent risk factors for a diagnosis of bacterial infection.…”

Are Orthopedic Surgeons Subjective Intraoperative Conclusions About Bone Mass Accurate?

“…diagnostic tools to determine the accuracy of the VAS score against bone status: osteoporosis or abnormal bone status (osteopenia or osteoporosis) [3,4]. Nonparametric methods were used to calculate the area under the ROC curves and bone status assessment was binary.…”