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 Experiment Videos

A generalized concordance correlation coefficient for continuous and categorical data.

T S King1, V M Chinchilli

  • 1Department of Health Evaluation Sciences, Division of Biostatistics, College of Medicine, The Pennsylvania State University, Hershey, PA 17033-0850, USA. tking@hes.hmc.psu.edu

Statistics in Medicine
|July 6, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Dietary Methionine and Total Sulfur Amino Acid Restriction in Healthy Adults.

The journal of nutrition, health & aging·2023
Same author

Sudden sensorineural hearing loss and coronavirus disease 2019.

The Journal of laryngology and otology·2022
Same author

Current variations and practice patterns in tympanic membrane perforation repair.

The Journal of laryngology and otology·2022
Same author

Internalized skin bias: validation study to explore the impact of the internalization of social stigma on those with hidradenitis suppurativa.

Journal of the European Academy of Dermatology and Venereology : JEADV·2022
Same author

From Theory to Practice: Measuring end-of-life communication quality using multiple goals theory.

Patient education and counseling·2016
Same author

A systematic review and meta-analysis of salivary cortisol measurement in domestic canines.

Domestic animal endocrinology·2016
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

This study generalizes the concordance correlation coefficient (CCC) for agreement data. New methods offer robust versions for continuous and categorical data, unifying agreement assessment.

Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • The concordance correlation coefficient (CCC) is a standard measure for assessing agreement between two continuous measures.
  • Existing CCC methods are sensitive to outliers and limited to continuous data.
  • There is a need for more robust and versatile agreement measures.

Purpose of the Study:

  • To generalize the concordance correlation coefficient (CCC) for enhanced robustness and broader applicability.
  • To extend CCC methodology to categorical data, bridging the gap with kappa statistics.
  • To introduce stratified and extended CCC versions for complex agreement assessments.

Main Methods:

  • Generalized the CCC using alternative distance functions for increased robustness.

Related Experiment Videos

  • Extended the generalized CCC to categorical data, comparing it with kappa and weighted kappa statistics.
  • Developed stratified and extended CCC versions to account for covariates and multiple raters.

    • Main Results:

    • The generalized CCC provides more robust agreement estimates compared to the standard CCC.
    • The generalized CCC for categorical data shows strong similarities to kappa and weighted kappa statistics.
    • Stratified and extended CCC versions effectively adjust for confounding factors and assess multi-rater agreement.

    Conclusions:

    • The generalized concordance correlation coefficient offers a unified and robust framework for agreement assessment.
    • This extended methodology is applicable to both continuous and categorical data, accommodating complex scenarios.
    • The findings advance statistical methods for evaluating measurement agreement across diverse data types.