Cited by 111Reliability and Agreement in MeasurementStatistical Methods and Bayesian InferenceStatistical Methods in Clinical Trials

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The Concordance Correlation Coefficient for Repeated Measures Estimated by Variance Components

Carrasco, Josep L., King, Tonya S., Chinchilli, Vernon M.·2009·Journal of Biopharmaceutical Statistics

DOI 10.1080/10543400802527890

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Abstract

The concordance correlation coefficient (CCC) is an index that is commonly used to assess the degree of agreement between observers on measuring a continuous characteristic. Here, a CCC for longitudinal repeated measurements is developed through the appropriate specification of the intraclass correlation coefficient from a variance components linear mixed model. A case example and the results of a simulation study are provided.

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Cressie, N. (1992) · Terra Nova · doi:10.1111/j.1365-3121.1992.tb00605.x · Cited by 9,139
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Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785
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Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507
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King, T. S., Chinchilli, V. M., & Carrasco, J. L. (2007) · Statistics in Medicine · doi:10.1002/sim.2778 · Cited by 114
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References
Choudhary, P. K., & Nagaraja, H. N. (2017) · Wiley series in probability and statistics · doi:10.1002/9781118553282.refs
Peer Review #1 of "lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time (v0.1)"
(2020) · PeerJ · doi:10.7287/peerj.9850v0.1/reviews/1
Peer Review #2 of "lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time (v0.1)"
(2020) · PeerJ · doi:10.7287/peerj.9850v0.1/reviews/2
Prognostic and Prediction Modelling with Radiomics for Non-Small Cell Lung Cancer
Patil Ravindra, B. (2020) · Research Publications (Maastricht University) · doi:10.26481/dis.20201006rp · Cited by 1
Peer Review #2 of "lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time (v0.2)"
(2020) · PeerJ · doi:10.7287/peerj.9850v0.2/reviews/2
Desarrollo de técnicas estadísticas basadas en computacion intensiva para el análisis de medidas repetidas
Sánchez-Espigares, J. A. (2021) · doi:10.5821/dissertation-2117-351135
Investigation of Carter method for locating slip surfaces and its performance on varying slope conditions
Novanti, N. A., Lu, C., Lin, T. S., Lee, W.-L., & Lin, Y. (2023) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1249/1/012006
Modeling a System of Adaptive Management of Manufacturing Enterprises in Ukraine: Political, Economic, Socio-cultural, and Technological Analysis
Kondratenko, N. O., Kovalenko, L., Cirella, G. T., Doroshenko, H., Babych, S., & Artiukh, T. O. (2024) · Contributions to economics · doi:10.1007/978-3-031-48735-4_6
Analyzing method comparison data with skew-normal measurement error models: incorporating generalized scale mixtures and varying degrees of freedom
Duwarahan, J., & Nawarathna, L. S. (2024) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2024.2366984
Concurrent Validity of Connected Insoles to Quantify Temporal Parameters of Single-Step Initiation in Healthy Young AdultsPreprint
Coudrat, L., Kop, J., Bascou, J., Mansour, K. B., Jacob, C., Jonveaux, T., Trousselard, M., & Dinet, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5008028
Reliability of Tracheal Temperature as a Measurement of Core Body Temperature During Cardiac Surgery Using Cardiopulmonary Bypass
Kang, H., Lee, S. H., Chin, J., Choi, I., & Kim, K. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020632
Concurrent validity of connected insoles to quantify temporal parameters of single-step initiation in healthy young adults
Coudrat, L., Kop, J., Bascou, J., Mansour, K. B., Jacob, C., Jonveaux, T., Trousselard, M., & Dinet, J. (2025) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2025.112705
Using Durometers to Quantify Stiffness as an Outcome Measure for Cutaneous Neurofibroma in Neurofibromatosis Type 1
Chamseddin, B. H., McKay, R. M., Jin, R., Zhu, H., & Le, L. Q. (2025) · JID Innovations · doi:10.1016/j.xjidi.2025.100380
Comparison of Two Miniaturized, Rectifiable Aerosol Photometers for Personal PM2.5 Monitoring in a Dusty Occupational Environment
Johnston, J. D., Collingwood, S. C., LeCheminant, J. D., Peterson, N. E., South, A. J., Farnsworth, C. B., Chartier, R., Thiel, M. E., Brown, T. P., Goss, E. S., et al. (2025) · Atmosphere · doi:10.3390/atmos16111233 · Cited by 1
Additional file 8 of Transcranial Doppler as a screening test to exclude intracranial hypertension in brain-injured patients: the IMPRESSIT-2 prospective multicenter international study
Rasulo, F. A., Calza, S., Robba, C., Taccone, F. S., Biasucci, D. G., Badenes, R., Piva, S., Savo, D., Citerio, G., Dibu, J. R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19607680
Additional file 6 of Transcranial Doppler as a screening test to exclude intracranial hypertension in brain-injured patients: the IMPRESSIT-2 prospective multicenter international study
Rasulo, F. A., Calza, S., Robba, C., Taccone, F. S., Biasucci, D. G., Badenes, R., Piva, S., Savo, D., Citerio, G., Dibu, J. R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19607674
Additional file 1 of Transcranial Doppler as a screening test to exclude intracranial hypertension in brain-injured patients: the IMPRESSIT-2 prospective multicenter international study
Rasulo, F. A., Calza, S., Robba, C., Taccone, F. S., Biasucci, D. G., Badenes, R., Piva, S., Savo, D., Citerio, G., Dibu, J. R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19607659
Additional file 3 of Transcranial Doppler as a screening test to exclude intracranial hypertension in brain-injured patients: the IMPRESSIT-2 prospective multicenter international study
Rasulo, F. A., Calza, S., Robba, C., Taccone, F. S., Biasucci, D. G., Badenes, R., Piva, S., Savo, D., Citerio, G., Dibu, J. R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19607665

Authors: Carrasco, J. L., King, T. S., & Chinchilli, V. M.
Affiliations: Universitat de Barcelona (ES)
Pennsylvania State University (US)
Corresponding author: Carrasco, Josep L.
ORCID: Carrasco, Josep L.
King, Tonya S.
Chinchilli, Vernon M.
Venue: Journal of Biopharmaceutical Statistics
Publisher: Informa UK Limited
Published: 2009-01-02
Volume: 19
Issue: 1
Pages: 90-105
ISSN: 1054-3406 (ISSN-L)
1520-5711
DOI: 10.1080/10543400802527890
OpenAlex: W2149541891
Cited by: 111
References: 13
Citations by year: 2026: 8 · 2025: 10 · 2024: 8 · 2023: 3 · 2022: 14 · 2021: 10 · 2020: 9 · 2019: 5 · 2018: 9 · 2017: 11 · 2016: 3 · 2015: 4 · 2014: 2 · 2013: 6 · 2012: 1
FWCI: 3.71
Type: journal article
Language: en
Open access: closed
Locations: Journal of Biopharmaceutical Statistics (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: concordance correlation coefficient, repeated measures, intraclass correlation coefficient, variance components, linear mixed models, interobserver agreement, longitudinal data, measurement agreement
MeSH terms: Algorithms, Analysis of Variance, Computer Simulation, Pharmaceutical Preparations, Humans, Hydrocortisone, Pharmacokinetics, Models, Statistical, Observer Variation, Confidence Intervals, Linear Models, Area Under Curve

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The Concordance Correlation Coefficient for Repeated Measures Estimated by Variance Components

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