Receiver operating characteristic curve: overview and practical use for clinicians

Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The p...

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Vydáno v:Korean journal of anesthesiology Ročník 75; číslo 1; s. 25 - 36
Hlavní autor: Nahm, Francis Sahngun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Korea (South) Korean Society of Anesthesiologists 01.02.2022
대한마취통증의학회
Témata:
area under curve
Humans
mathematics
reference values
Research Design
ROC Curve
routine diagnostic tests
Statistical Round
statistics
마취과학
Mathematics
Research design
ROC curve
Statistics
Reference values
Routine diagnostic tests
Area under curve
ISSN:2005-6419, 2005-7563, 2005-7563
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Shrnutí:Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining the presence or absence of a disease. Although clinicians who do not have expertise in statistics do not need to understand both the complex mathematical equation and the analytic process of ROC curves, understanding the core concepts of the ROC curve analysis is a prerequisite for the proper use and interpretation of the ROC curve. This review describes the basic concepts for the correct use and interpretation of the ROC curve, including parametric/nonparametric ROC curves, the meaning of the area under the ROC curve (AUC), the partial AUC, methods for selecting the best cut-off value, and the statistical software to use for ROC curve analyses.
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https://doi.org/10.4097/kja.21209
ISSN:2005-6419
2005-7563
2005-7563
DOI:10.4097/kja.21209