Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Region of Convergence01:17

Region of Convergence

The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Dose Response Curve: Conventional Versus Nonmonotonic01:21

Dose Response Curve: Conventional Versus Nonmonotonic

The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response relationships...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Risk, Predictors, and Outcomes of Acute Kidney Injury in Patients Admitted to Intensive Care Units in Egypt.

Scientific reports·2017
Same author

Long-Term Progression of Coronary Artery Calcification Is Independent of Classical Risk Factors, C-Reactive Protein, and Parathyroid Hormone in Renal Transplant Patients.

Cardiorenal medicine·2017
Same author

Obesity and kidney disease: Hidden consequences of the epidemic.

African journal of primary health care & family medicine·2017
Same author

Validation of echocardiographic criteria for the clinical diagnosis of heart failure in chronic kidney disease.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association·2017
Same author

Optimizing hypertension management in renal transplantation: a call to action.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association·2017
Same author

Optimizing hypertension management in renal transplantation: a call to action.

Journal of hypertension·2017

Related Experiment Video

Updated: Jun 23, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Diagnostic methods 2: receiver operating characteristic (ROC) curves.

Giovanni Tripepi1, Kitty J Jager, Friedo W Dekker

  • 1CNR-IBIM, Clinical Epidemiology and Physiopathology of Renal Diseases and Hypertension of Reggio Calabria, Reggio Calabria, Italy. gtripepi@ibim.cnr.it

Kidney International
|May 21, 2009
PubMed
Summary
This summary is machine-generated.

Receiver operating characteristic (ROC) curve analysis is crucial for evaluating diagnostic tests and identifying optimal cutoff values. While valuable for prognosis, ROC curves may differ from survival analysis when time-to-event data is involved.

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Related Experiment Videos

Last Updated: Jun 23, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Epidemiology

Background:

  • Clinical decisions often require dichotomizing continuous test results.
  • Accurate assessment of diagnostic and prognostic tests is essential in healthcare.

Purpose of the Study:

  • To explain the application and utility of Receiver Operating Characteristic (ROC) curve analysis.
  • To highlight the role of ROC curves in evaluating diagnostic accuracy, prognostic value, and risk scores.
  • To identify limitations of ROC curves in prognostic scenarios involving time-to-event data.

Main Methods:

  • Receiver Operating Characteristic (ROC) curve analysis.
  • Evaluation of diagnostic accuracy and discrimination performance.
  • Comparison of predictive values of biomarkers and risk scores.

Main Results:

  • ROC curve analysis effectively assesses quantitative diagnostic tests across their value range.
  • It aids in identifying optimal cutoff values for dichotomous decisions.
  • ROC curves can evaluate prognostic biomarkers and multivariate risk scores.

Conclusions:

  • ROC curve analysis is a versatile tool for diagnostic and prognostic assessments.
  • It is valuable for converting continuous tests into dichotomous ones.
  • When applied to prognosis, ROC curves have limitations regarding time-to-event and censoring, potentially differing from survival analyses.