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

Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

1.0K
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
1.0K
Coefficient of Correlation01:12

Coefficient of Correlation

8.7K
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...
8.7K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.0K
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...
5.0K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

4.9K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
4.9K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.3K
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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Consistency of c-Met protein overexpression over time in patients with non-squamous non-small cell lung cancer.

Histopathology·2026
Same author

Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

The association between residential natural vegetation exposure at diagnosis and colorectal cancer-specific mortality among colorectal cancer cases in Kentucky.

Environmental research·2025
Same author

Conformal Selection for Efficient and Accurate Compound Screening in Drug Discovery.

Journal of chemical information and modeling·2025
Same author

A multi-ethnic polygenic risk score for chronic kidney disease is associated with increased risk of hypertension in African American individuals.

BMC nephrology·2025
Same author

Prospective real-world evaluation of t(11;14) prevalence and disease biology in multiple myeloma: MEDICI study analysis.

Blood advances·2025
Same journal

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis.

The international journal of biostatistics·2026
Same journal

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026
Same journal

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same journal

Handling the uncertainty issue of missingness via a mixture-structure-based method.

The international journal of biostatistics·2026
Same journal

Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

The international journal of biostatistics·2026
Same journal

Prognostic score methods for the estimation of the average causal effect.

The international journal of biostatistics·2026
See all related articles

Related Experiment Video

Updated: Feb 12, 2026

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

11.3K

A Bayesian Framework for Estimating the Concordance Correlation Coefficient Using Skew-elliptical Distributions.

Dai Feng1, Richard Baumgartner1, Vladimir Svetnik1

  • 1Merck & Co., Inc, Rahway, NJ, United States of America.

The International Journal of Biostatistics
|April 6, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a flexible Bayesian framework for estimating the concordance correlation coefficient (CCC), enhancing agreement analysis for diverse data types and practical challenges like missing data.

Keywords:
MCMCconcordance correlation coefficientmultivariate normal and t distributionsmultivariate skew normal and t distributions

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
09:34

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

7.8K

Related Experiment Videos

Last Updated: Feb 12, 2026

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

11.3K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
09:34

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

7.8K

Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • The concordance correlation coefficient (CCC) is a crucial metric for assessing agreement between measurements.
  • Existing methods for CCC estimation may have limitations in handling complex data structures and real-world data issues.

Purpose of the Study:

  • To propose a unified Bayesian framework for estimating the CCC.
  • To develop a method that accommodates various data distributions (symmetric/asymmetric, light/heavy-tailed).
  • To integrate model selection and handle confounding covariates and missing data within the CCC estimation framework.

Main Methods:

  • A unified Bayesian statistical framework was developed for CCC estimation.
  • The framework allows for the accommodation of diverse data characteristics.
  • Methods for model selection and handling of confounding covariates and missing data were incorporated.

Main Results:

  • The proposed Bayesian framework demonstrated robust performance in estimating the CCC.
  • Simulated data analysis confirmed the framework's effectiveness across different data scenarios.
  • Real-life biomarker data from an insomnia drug clinical study validated the practical applicability of the method.

Conclusions:

  • The proposed unified Bayesian framework offers a flexible and comprehensive approach to CCC estimation.
  • This method effectively addresses common challenges in agreement analysis, including data heterogeneity and missingness.
  • The availability of an R package facilitates the application of this advanced statistical technique in research.