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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
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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

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