Comparison of concordance correlation coefficient via variance components, generalized estimating equations and weighted approaches with model selection

Variance components (VC) and generalized estimating equations (GEE) are two approaches for estimating concordance correlation coefficients (CCC) adjusting for covariates, and allowing dependency between replicated samples. However, under VC and GEE, a model including all potential explanatory variables may lead to biased parameter estimates. To overcome this problem, the estimation of CCC using VC and GEE approaches, as well as applying the conditional Akaike information criterion (CAIC) and the quasi-likelihood under the independence model criterion (QIC) measures for model selection is applied. The weighted approach which is the most efficient estimator of CCC obtained by combining the estimators from VC and GEE is also proposed. Simulation studies are conducted to compare the performance of the VC and the GEE, both with and without model-selection via CAIC and QIC, respectively, and the weighted approaches for dependent continuous data. Two applications are illustrated: an assessment of conformity between two optometric devices and an evaluation of agreement in degree of myopia for dizygotic twins. To conclude, the CAIC and QIC model-selection procedures embedded in VC and GEE approaches, respectively, can provide more satisfactory results than VC and GEE involving all possible covariates. Furthermore, the weighted approach is a reliable and stable procedure with the smallest mean square errors and nominal 95% coverage rates in estimating CCC.