Modeling Concordance Correlation Coefficient for Longitudinal Study Data
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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.
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- Modeling Concordance Correlation Coefficient for Longitudinal Study Data
Volume 75, Issue 1 , pp 99-119
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- diagnostic test
- inverse probability weighted estimates
- missing data
- monotone missing data pattern
- Industry Sectors
- Author Affiliations
- 1. Department of Public Health, Hospital for Special Surgery–Weill Medical College of Cornell University, New York, NY, 10021, USA
- 2. Department of Biostatistics and Computational Biology, Department of Psychiatry, University of Rochester, Rochester, NY, 14642, USA
- 3. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, 14642, USA
- 4. Department of Biostatistics and Computational Biology, Department of Psychiatry, University of Rochester, Rochester, NY, 14642, USA