Psychometrika

, Volume 75, Issue 1, pp 99–119

Modeling Concordance Correlation Coefficient for Longitudinal Study Data

Authors

    • Department of Public HealthHospital for Special Surgery–Weill Medical College of Cornell University
  • Wan Tang
    • Department of Biostatistics and Computational Biology, Department of PsychiatryUniversity of Rochester
  • Qin Yu
    • Department of Biostatistics and Computational BiologyUniversity of Rochester
  • X. M. Tu
    • Department of Biostatistics and Computational Biology, Department of PsychiatryUniversity of Rochester
Theory and Methods

DOI: 10.1007/s11336-009-9142-z

Cite this article as:
Ma, Y., Tang, W., Yu, Q. et al. Psychometrika (2010) 75: 99. doi:10.1007/s11336-009-9142-z

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.

diagnostic test inverse probability weighted estimates missing data monotone missing data pattern U-statistics

Copyright information

© The Psychometric Society 2009