[ROC (receiver operating characteristics) curve: principles and application in biology].
Laboratory test's diagnostic performances are generally estimated by means of their sensibility, specificity and positive and negative predictive values. Unfortunately, these indices reflect only imperfectly the capacity of a test to correctly classify subjects into clinically relevant subgroups. The appeal to ROC (receiver operating characteristic) curve appears as a tool of choice for this evaluation. Used in the medical domain since the 60s, ROC curve is a graphic representation of the relation existing between the sensibility and the specificity of a test, calculated for all possible cut-off. It allows the determination and the comparison of the diagnostic performances of several tests. It is also used to consider the optimal cut-off of a test, by taking into account epidemiological and medical - economic data of the disease. Used in numerous medical domains, this statistical tool is easily accessible thanks to the development of computer softwares. This article exposes the principles of construction and exploitation of a ROC curve.
Delacour
H
H
Laboratoire de biochimie, toxicologie cliniques, Hôpital d'Instruction des Armées du Val-de-Grâce, Paris, France. h_delacour@yahoo.fr
Servonnet
A
A
Perrot
A
A
Vigezzi
J F
JF
Ramirez
J M
JM
D000230:Adenocarcinoma / Q000097:blood / Q000175:diagnosis; D003933:Diagnosis; D003937:Diagnosis, Differential; D003955:Diagnostic Tests, Routine; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006304:Health Status; D006801:Humans; D020407:Internet; D016013:Likelihood Functions; D008297:Male; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011470:Prostatic Hyperplasia / Q000097:blood / Q000175:diagnosis; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012984:Software; D018709:Statistics, Nonparametric
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