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...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Scatter Plot01:15

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...

You might also read

Related Articles

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

Sort by
Same author

Joint modelling of competing risks and current status data: an application to a spontaneous labour study.

Journal of the Royal Statistical Society. Series C, Applied statistics·2025
Same author

Modeling the age-specific incidence of mild cognitive impairment incorporating the time-varying relationship of Alzheimer's disease biomarkers over 28 years.

Journal of Alzheimer's disease : JAD·2025
Same author

Change points for dynamic biomarkers in the Alzheimer's disease pathological cascade: A 30-year cohort study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

15th Annual University of Pennsylvania conference on statistical issues in clinical trial/advances in time to event analyses in clinical trials (morning panel discussion).

Clinical trials (London, England)·2024
Same author

Joint Associations of Pregnancy Complications and Postpartum Maternal Renal Biomarkers With Severe Cardiovascular Morbidities: A US Racially and Ethnically Diverse Prospective Birth Cohort Study.

Journal of the American Heart Association·2023
Same author

Cerebrospinal Fluid Alzheimer's Disease Biomarker Patterns of Change Prior to the Onset of Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD·2023
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: May 18, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Bivariate marker measurements and ROC analysis.

Mei-Cheng Wang1, Shanshan Li

  • 1Department of Biostatistics Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. mcwang@jhsph.edu

Biometrics
|September 26, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces novel receiver operating characteristic (ROC) analysis methods for bivariate markers, enhancing predictive accuracy evaluation using tree-based classifiers and and-or logic. These methods improve biomarker predictability assessment in populations.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Related Experiment Videos

Last Updated: May 18, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Machine Learning

Background:

  • Traditional receiver operating characteristic (ROC) analysis primarily focuses on univariate marker data.
  • Evaluating the predictive accuracy of multiple biomarkers simultaneously presents significant analytical challenges.

Purpose of the Study:

  • To extend ROC analysis methodologies to accommodate bivariate marker measurements.
  • To develop novel tree-based classification rules for assessing the performance of bivariate markers.

Main Methods:

  • Introduction of an and-or classifier framework for bivariate marker analysis.
  • Development of novel ROC and weighted ROC (WROC) functions tailored for bivariate data.
  • Application of nonparametric methods for estimating ROC-related statistics, including area under the curve and concordance probability.

Main Results:

  • Proposed ROC and WROC functions effectively evaluate and-or classifier performance across all marker value combinations.
  • Nonparametric estimation methods provide reliable measures of marker predictability for bivariate data.
  • The developed inferential results are applicable to single, bivariate, and multivariate marker settings.

Conclusions:

  • The novel ROC analysis framework offers enhanced tools for evaluating bivariate marker predictability.
  • These methods provide a robust approach for comparing marker performance and classifier designs.
  • The findings extend to more complex multivariate marker systems with combined classifiers.