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

569
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...
569
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.7K
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...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Machine learning for predicting institutionalization and mortality risks among older home care recipients with routinely collected need assessment data: explainable AI for long-term care.

BMC medical informatics and decision making·2026
Same author

Causal Inference in the Presence of Missing Outcome and Treatment Variables: Triply Robust Estimator and Sensitivity Analysis.

Statistics in medicine·2026
Same author

Robust Estimation of Population Attributable Fractions in the Presence of Multiple Ordered Mediators.

Statistics in medicine·2026
Same author

Response to: 'Methodological considerations in the analysis of time-to-event outcomes with MAIHDA' by Bashir and Al-Kassab-Córdova.

Journal of epidemiology and community health·2026
Same author

Abdominopelvic computed tomography during pregnancy and the risk of congenital malformations: protocol for a nationwide population-based cohort study in South Korea.

BMJ open·2026
Same author

On the robustness of truncated negative binomial regression model: application to field epidemiology.

Journal of applied statistics·2026

Related Experiment Video

Updated: Mar 18, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Evaluating Diagnostic Accuracy of Binary Medical Tests in Multi-Reader Multi-Case Study.

Seungjae Lee1,2, Sowon Jang3, Woojoo Lee2,4

  • 1Department of Applied Statistics, Kyonggi University, Suwon, Republic of Korea.

Statistics in Medicine
|March 17, 2026
PubMed
Summary
This summary is machine-generated.

Multi-reader multi-case (MRMC) studies compare diagnostic performance. Conditional logistic regression offers a robust analytical approach for complex MRMC data, improving sensitivity and specificity comparisons.

Keywords:
Cochran'sMcNemar's testbinary diagnostic testconditional logistic regressionmulti‐reader multi‐casesensitivity and specificity

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.6K

Related Experiment Videos

Last Updated: Mar 18, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.6K

Area of Science:

  • Medical imaging analysis
  • Biostatistics
  • Diagnostic test evaluation

Background:

  • Multi-reader multi-case (MRMC) studies are crucial for evaluating medical diagnostic performance.
  • Analyzing the complex correlations in MRMC data presents significant challenges.
  • Commonly used methods include generalized estimating equations, generalized linear mixed models, and McNemar's test.

Purpose of the Study:

  • To explain the theoretical properties of conditional logistic regression for MRMC studies.
  • To explore the relationship between conditional logistic regression, Cochran's Q, and McNemar's tests.
  • To provide a robust analytical method for comparing diagnostic performance in MRMC settings.

Main Methods:

  • Theoretical explanation of conditional logistic regression applied to MRMC data.
  • Exploration of the statistical properties and relationships with existing tests.
  • Extensive simulation studies and real-world data analysis to validate the method.

Main Results:

  • Conditional logistic regression provides a theoretically sound framework for MRMC analysis.
  • Demonstrated relationship between conditional logistic regression and established statistical tests.
  • Validation of the proposed method's performance through simulations and real data.

Conclusions:

  • Conditional logistic regression is a valuable tool for analyzing MRMC studies.
  • This method enhances the comparison of sensitivities and specificities across imaging modalities.
  • The study offers a more robust approach to handling complex correlations in diagnostic performance evaluation.