Hostname: page-component-89b8bd64d-rbxfs Total loading time: 0 Render date: 2026-05-08T02:15:27.828Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling Concordance Correlation Coefficient for Longitudinal Study Data

Published online by Cambridge University Press:  01 January 2025

Yan Ma ,
Wan Tang ,
Qin Yu  and
X. M. Tu
Yan Ma*
Affiliation:
Hospital for Special Surgery–Weill Medical College of Cornell University
Wan Tang
Affiliation:
University of Rochester
Qin Yu
Affiliation:
University of Rochester
X. M. Tu
Affiliation:
University of Rochester
*
Requests for reprints should be sent to Yan Ma, Department of Public Health, Hospital for Special Surgery–Weill Medical College of Cornell University, New York, NY 10021, USA. E-mail: yam2007@med.cornell.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Measures of agreement are used in a wide range of behavioral, biomedical, psychosocial, and health-care related research to assess reliability of diagnostic test, psychometric properties of instrument, fidelity of psychosocial intervention, and accuracy of proxy outcome. The concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. In modern-day applications, data are often clustered, making inference difficult to perform using existing methods. In addition, as longitudinal study designs become increasingly popular, missing data have become a serious issue, and the lack of methods to systematically address this problem has hampered the progress of research in the aforementioned fields. In this paper, we develop a novel approach to tackle the complexities involved in addressing missing data and other related issues for performing CCC analysis within a longitudinal data setting. The approach is illustrated with both real and simulated data.

Keywords

diagnostic testinverse probability weighted estimatesmissing datamonotone missing data patternU-statistics

Information

Type
Theory and Methods
Information
Psychometrika , Volume 75 , Issue 1 , March 2010 , pp. 99 - 119
Copyright
Copyright © 2010 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Barnhart, H.X., & Williamson, J.M. (2001). Modelling concordance correlation via GEE to evaluate reproducibility. Biometrics, 57, 931–940.CrossRefGoogle ScholarPubMed
Barnhart, H.X., Haber, M., & Song, J. (2002). Overall concordance correlation coefficient for evaluating agreement among multiple observers. Biometrics, 58, 1020–1027.CrossRefGoogle ScholarPubMed
Bauer, S., & Kennedy, J.W. (1981). Applied statistics for the clinical laboratory: II. Within-run imprecision. The Journal of Clinical Laboratory Automation, 1, 197–201.Google Scholar
Chandler, J.M., Martin, A.R., Girman, C., Ross, P.D., Love-McClung, B., Lydick, E., & Yawn, B.P. (1998). Reliability of an osteoporosis-targeted quality of life survey instrument for use in the community: OPTQoL. Osteoporosis International, 8, 127–135.CrossRefGoogle Scholar
Chinchilli, V.M., Martel, J.K., Kumanyika, S., & Lloyd, T. (1996). A weighted concordance correlation coefficient for repeated measures designs. Biometrics, 52, 341–353.CrossRefGoogle Scholar
Costa, P., Arnould, B., Cour, F., Boyer, P., Marrel, A., Jaudinot, E.O., & Solesse de Gendre, A. (2003). Quality of Sexual Life Questionnaire (QVS): a reliable, sensitive and reproducible instrument to assess quality of life in subjects with erectile dysfunction. International Journal of Impotence Research, 15, 173–184.CrossRefGoogle ScholarPubMed
Fieller, E.C. (1954). Some problems in interval estimation. Journal of the Royal Statistical Society B, 16, 175–185.CrossRefGoogle Scholar
Guo, X., Pan, W., Connett, J.E., Hannan, P.J., & French, S.A. (2005). Small-sample performance of the robust score test and its modifications in generalized estimating equations. Statistics in Medicine, 24, 3479–3495.CrossRefGoogle ScholarPubMed
King, T.S., Chinchilli, V.M. (2001). A generalized concordance correlation coefficient for continuous and categorical data. Statistics in Medicine, 20, 2131–2147.CrossRefGoogle ScholarPubMed
King, T.S., Chinchilli, V.M., & Carrasco, J.L. (2007). A repeated measures concordance correlation coefficient. Statistics in Medicine, 26, 3095–3113.CrossRefGoogle ScholarPubMed
Kowalski, J., & Tu, X.M. (2007). Modern applied U-statistics, New York: Wiley.CrossRefGoogle Scholar
Lin, L. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45, 255–268.CrossRefGoogle ScholarPubMed
Little, R.J.A., & Rubin, D.B. (1987). Statistical analysis with missing data, New York: Wiley.Google Scholar
Lloyd, T., Chinchilli, V.M., Eggli, D.F., Rollings, N., & Kulin, H.E. (1998). Body composition development of adolescent white females. Archives of Pediatric Adolescence Medicine, 152, 998–1002.Google ScholarPubMed
McCullagh, P., & Nelder, J.A. (1989). Generalized linear models, (2nd ed.). London: Chapman and Hall.CrossRefGoogle Scholar
McGraw, K.O., & Wong, S.P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1, 30–46.CrossRefGoogle Scholar
Morrison-Beedy, D., Carey, M.P., & Tu, X.M. (2006). Accuracy of audio computer-assisted self-interviewing (ACASI) and self-administered questionnaires for the assessment of sexual behavior. AIDS and Behavior, 10, 541–552.CrossRefGoogle ScholarPubMed
Paul, I.M., Wai, K., Jewell, S.J., Shaffer, M.L., & Varadan, V.V. (2006). Evaluation of a new self-contained, ambulatory, objective cough monitor. Cough, 2, 1–7.CrossRefGoogle ScholarPubMed
Prentice, R.L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics, 44, 321–327.CrossRefGoogle ScholarPubMed
Reboussin, B.A., & Liang, K.Y. (1998). An estimating equations approach for the LISCOMP model. Psychometrika, 63, 165–182.CrossRefGoogle Scholar
Robins, J.M., Rotnitzky, A., & Zhao, L.P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90, 106–121.CrossRefGoogle Scholar
Schroder, K.E.E., Carey, M.P., & Vanable, P.A. (2003). Methodological challenges in research on sexual risk behavior: II. Accuracy of self-reports. Annals of Behavioral Medicine, 26, 104–123.CrossRefGoogle ScholarPubMed
Seber, G.A.F. (1984). Multivariate observations, New York: Wiley.CrossRefGoogle Scholar
Serfling, R.J. (1980). Approximation theorems of mathematical statistics, New York: Wiley.CrossRefGoogle Scholar
Shrier, L.A., Shih, M., & Beardslee, W.R. (2005). Comparison of momentary sampling with diary and retrospective self-report methods of measurement. Pediatrics, 115, 573–581.CrossRefGoogle ScholarPubMed
Shrout, P.E., & Fleiss, J.L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86, 420–428.CrossRefGoogle ScholarPubMed
Tsiatis, A.A. (2006). Semiparametric Theory and Missing Data, New York: Springer.Google Scholar
Tu, X.M., Feng, C., Kowalski, J., Tang, W., Wang, H., Wan, C., & Ma, Y. (2007). Correlation analysis for longitudinal data: applications to HIV and psychosocial research. Statistics in Medicine, 26, 4116–4138.CrossRefGoogle ScholarPubMed
Westgard, J.O., & Hunt, M.R. (1973). Use and interpretation of common statistical tests on method-comparison studies. Clinical Chemistry, 19, 49–57.CrossRefGoogle ScholarPubMed