Psychometrika

, Volume 75, Issue 1, pp 99–119

Modeling Concordance Correlation Coefficient for Longitudinal Study Data

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

Authors and Affiliations

  1. 1.Department of Public HealthHospital for Special Surgery–Weill Medical College of Cornell UniversityNew YorkUSA
  2. 2.Department of Biostatistics and Computational Biology, Department of PsychiatryUniversity of RochesterRochesterUSA
  3. 3.Department of Biostatistics and Computational BiologyUniversity of RochesterRochesterUSA
  4. 4.Department of Biostatistics and Computational Biology, Department of PsychiatryUniversity of RochesterRochesterUSA

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