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

189
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...
189
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

195
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...
195
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

199
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
199
Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.5K
Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.6K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Covariate hypothesis tests for the cure rate in mixture cure models based on martingale difference correlation.

Statistical methods in medical research·2026
Same author

Modelling dependent censoring in time-to-event data using boosting copula regression.

Lifetime data analysis·2025
Same author

A New Test for Assessing the Covariate Effect in ROC Curves.

Statistics in medicine·2025
Same author

Testing for the Functional Form of a Continuous Covariate in the Shared-Parameter Joint Model.

Statistics in medicine·2025
Same author

Testing for Sufficient Follow-Up in Censored Survival Data by Using Extremes.

Biometrical journal. Biometrische Zeitschrift·2024
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Jul 6, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

A test for comparing conditional ROC curves with multidimensional covariates.

A Fanjul-Hevia1, J C Pardo-Fernández2, I Van Keilegom3

  • 1Departamento de Estadística e Investigación Operativa y Didáctica de la Matemáitica, Universidad de Oviedo, Oviedo, Spain.

Journal of Applied Statistics
|January 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical test to compare multiple dependent Receiver Operating Characteristic (ROC) curves, accounting for covariates. The method is validated through simulations and applied to analyze diagnostic markers for Pleural Effusion.

Keywords:
BootstrapROC curvescovariateshypothesis testingprojections

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

290

Related Experiment Videos

Last Updated: Jul 6, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

290

Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Modeling

Background:

  • Receiver Operating Characteristic (ROC) curve analysis is standard for evaluating classification procedures.
  • Covariates can influence diagnostic variable performance, necessitating their inclusion in comparisons.
  • Existing methods may not adequately address dependent ROC curves in the presence of multidimensional covariates.

Purpose of the Study:

  • To propose a novel non-parametric test for comparing two or more dependent ROC curves.
  • To develop a method that accounts for the influence of multidimensional covariates.
  • To assess the practical performance of the proposed methodology.

Main Methods:

  • A new non-parametric statistical test is introduced.
  • The test handles dependent ROC curves conditioned on multidimensional covariates.
  • Projections are employed to simplify the problem into a one-dimensional approach for analysis.

Main Results:

  • Simulations demonstrate the practical utility and performance of the new methodology.
  • The procedure effectively compares diagnostic capabilities while controlling for covariates.
  • The method is successfully applied to a real-world dataset.

Conclusions:

  • The proposed non-parametric test offers a robust approach for comparing dependent ROC curves.
  • Accounting for covariates is crucial for accurate performance evaluation of diagnostic markers.
  • The methodology provides a valuable tool for analyzing complex diagnostic data, as shown in the Pleural Effusion study.