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

313
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...
313
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

611
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...
611
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.0K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

267
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
267
Bonferroni Test01:10

Bonferroni Test

2.8K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.8K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

253
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
253

You might also read

Related Articles

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

Sort by
Same author

Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.

JMIR medical informatics·2026
Same author

Effectiveness and safety of melatoninergic agonists in preventing delirium in the ICU: an updated dose‒response meta-analysis of randomized controlled trials.

Critical care (London, England)·2026
Same author

Discharge Cognitive-Motor Imbalance Patterns and Long-Term Outcomes After Traumatic Brain Injury: A Propensity Score-Matched Cohort Study.

Journal of clinical medicine·2026
Same author

Adjunctive Antipsychotics in Major Depressive Disorder: A Systematic Review and Network Meta-Analysis.

JAMA psychiatry·2026
Same author

Ketamine Infusions and Rapid Reduction of Suicidal and Depressive Symptoms in Major Depressive Episode: A Systematic Review and Meta-Analysis.

JAMA psychiatry·2026
Same author

Clinical effects of ursodeoxycholic acid in COVID-19 infection: a systematic review and dose-response meta-analysis.

Frontiers in pharmacology·2026

Related Experiment Video

Updated: Aug 25, 2025

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

869

Meta-analysis of diagnostic test accuracy studies with multiple thresholds for data integration.

Sung Ryul Shim1

  • 1Department of Health and Medical Informatics, Kyungnam University College of Health Sciences, Changwon, Korea.

Epidemiology and Health
|October 13, 2022
PubMed
Summary

This study introduces methods for integrating multiple diagnostic test accuracy (DTA) thresholds in meta-analysis. The diagmeta R package is recommended for its ability to utilize all cut-off values, improving accuracy synthesis.

Keywords:
Diagnostic test accuracyDiagnostic testsEvidence based medicineMeta-analysisSystematic reviewThreshold

More Related Videos

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

7.6K
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

504

Related Experiment Videos

Last Updated: Aug 25, 2025

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

869
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

7.6K
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

504

Area of Science:

  • Medical Statistics
  • Diagnostic Test Accuracy Research
  • Health Informatics

Background:

  • Integrating multiple cut-off values in diagnostic test accuracy (DTA) meta-analyses presents challenges for data synthesis.
  • Existing methods often omit data from studies reporting multiple thresholds, potentially biasing results.

Approach:

  • Compared univariate and bivariate meta-analysis methods using R packages (meta, mada, diagmeta).
  • Utilized 13 prostate cancer DTA studies with 34 effect sizes, including various cut-off values.
  • Analyzed summary statistics and receiver operating characteristic curves to evaluate method performance.

Key Points:

  • Univariate analysis (meta package) and bivariate analysis (mada package) with single cut-offs yielded similar results.
  • Bivariate analysis using the diagmeta package, incorporating all cut-offs, demonstrated improved sensitivity and specificity with increased data.
  • The diagmeta package effectively handles heterogeneity and utilizes all available cut-off data.

Conclusions:

  • The bivariate analysis model within the diagmeta R package is recommended for DTA meta-analysis when multiple cut-offs are present.
  • This practical approach facilitates easier DTA determination for non-statistician researchers using R software.
  • Encourages wider adoption of comprehensive DTA meta-analysis methods to enhance research accuracy.