Concordance correlation coefficients estimated by generalized estimating equations and variance components for longitudinal repeated measurements

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3 Citations (Scopus)

Abstract

The concordance correlation coefficient (CCC) is a commonly accepted measure of agreement between two observers for continuous responses. This paper proposes a generalized estimating equations (GEE) approach allowing dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously. Furthermore, the indices of intra-, inter-, and total agreement through variance components (VC) from an extended three-way linear mixed model (LMM) are also developed with consideration of the correlation structure of longitudinal repeated measurements. Simulation studies are conducted to compare the performance of the GEE and VC approaches for repeated measurements from longitudinal data. An application of optometric conformity study is used for illustration. In conclusion, the GEE approach allowing flexibility in model assumptions and correlation structures of repeated measurements gives satisfactory results with small mean square errors and nominal 95% coverage rates for large data sets, and when the assumption of the relationship between variances and covariances for the extended three-way LMM holds, the VC approach performs outstandingly well for all sample sizes.

Original languageEnglish
Pages (from-to)1319-1333
Number of pages15
JournalStatistics in Medicine
Volume36
Issue number8
DOIs
Publication statusPublished - 2017 Apr 15

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Repeated Measurements
Generalized Estimating Equations
Variance Components
Concordance
Correlation coefficient
Linear Models
Observer
Linear Mixed Model
Correlation Structure
Sample Size
Longitudinal Data
Large Data Sets
Mean square error
Categorical or nominal
Coverage
Flexibility
Simulation Study
Datasets

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Cite this

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abstract = "The concordance correlation coefficient (CCC) is a commonly accepted measure of agreement between two observers for continuous responses. This paper proposes a generalized estimating equations (GEE) approach allowing dependency between repeated measurements over time to assess intra-agreement for each observer and inter- and total agreement among multiple observers simultaneously. Furthermore, the indices of intra-, inter-, and total agreement through variance components (VC) from an extended three-way linear mixed model (LMM) are also developed with consideration of the correlation structure of longitudinal repeated measurements. Simulation studies are conducted to compare the performance of the GEE and VC approaches for repeated measurements from longitudinal data. An application of optometric conformity study is used for illustration. In conclusion, the GEE approach allowing flexibility in model assumptions and correlation structures of repeated measurements gives satisfactory results with small mean square errors and nominal 95{\%} coverage rates for large data sets, and when the assumption of the relationship between variances and covariances for the extended three-way LMM holds, the VC approach performs outstandingly well for all sample sizes.",
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