Estimating the Generalized Concordance Correlation Coefficient through Variance Components

Carrasco, Josep L.; Jover, Lluís

doi:10.1111/j.0006-341x.2003.00099.x

Cited by 207 publications

(238 citation statements)

References 17 publications

(47 reference statements)

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“…An acceptable agreement (CCC) 18 was observed between the microarray results and the qPCR results. There is a very small or no overlap between the list of validated genes identified in the current study and those reported previously.…”

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confidence: 70%

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Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness

Pedraza

Gómez-Capilla

Escaramís

et al. 2009

Cancer

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BACKGROUND:The development of reliable gene expression profiling technology is having an increasing impact on the understanding of breast cancer biology. METHODS: In this study, microarray analysis was performed to establish gene signatures for different breast cancer phenotypes, to determine differentially expressed gene sequences at different stages of the disease, and to identify sequences with biologic significance for tumor progression. Samples were taken from patients before their treatment. After microarray analysis, the expression level of 153 selected genes was studied by real-time quantitative polymerase chain reaction analysis. RESULTS: Several gene sequences were expressed differentially in tumor samples versus control samples and also were associated with different breast cancer phenotypes, estrogen receptor status, tumor histology, and grade of tumor differentiation. In lymph node-negative tumors were identified a set of genes related to tumor differentiation grade. CONCLUSIONS: Several differentially expressed gene sequences were identified at different stages of breast cancer. Cancer 2010;116:486-96.

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confidence: 70%

“…An agreement analysis between microarray and RT-PCR FC values was performed for each condition using the concordance correlation coefficient 18 (CCC), which yields values between 0 (independence) and 1 (perfect agreement). …”

Section: Concordance Between Microarray and Real-time Pcr Resultsmentioning

confidence: 99%

Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness

Pedraza

Gómez-Capilla

Escaramís

et al. 2009

Cancer

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“…In order to assess the agreement between the assays, the first step is to model the data. The Linear mixed effects model is most commonly used in modeling the method comparison data [6][7][8][9][10][11][12][13][14][15]. Because of the flexibility in modeling of within subject dependence, linear mixed models are popular.…”

Section: Introductionmentioning

confidence: 99%

Assessing inter-rater agreement between multiple medical instruments with heteroscedastic measurements

Dassanayake¹,

Nawarathna²

2017

J Med Stat Inform

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Background: In clinical medicine, agreement evaluation plays a major role in determining the compatibility and the accuracy of newly introduced methods with pre-existing methods. These methods may be assays, clinical observers, medical devices etc. It is vital to assess the compatibility and the accuracy of these newly introduced techniques because they deal with the measurements of the human body, such as blood pressure, cholesterol level, heart rate etc. In practice, agreement evaluation is carried out among two methods of measurements and deals with the data that are homoscedastic. The main objective of this study is to extend the standard mixed model to allow the error variances to depend on magnitude of measurement and evaluate agreement between multiple methods assuming the new model, taking the heteroscedasticity into account. Methods: In order to assess the agreement, there are two typical steps in method comparison studies. The first step is to model the data using the Heteroscedastic mixed effects model. The model fitting is carried out by using two main approaches, namely the mean method and the best linear unbiased predictor method. After fitting the model for the second step, the agreement evaluation is carried out using Concordance correlation coefficient and Total deviation index. Results: The illustrative example contained five methods of measurements and was with heteroscedastic measurements. First, the model fitting was carried out according to the two approaches and the resulting parameters were almost identical. After the model fitting, the agreement evaluation was performed. According to the values resulted from the agreement measurements, it is clear that all five methods agree sufficiently well with the reference method. Conclusions: The proposed model can be used to model the method comparison data with heteroscedastic measurements with multiple methods of measurements as well as balanced and unbalanced data designs. Under the proposed model, the agreement evaluation methodology for comparing multiple methods is also developed taking heteroscedasticity into account.

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“…Several methods have been proposed to model the method comparison data, among them the regression-based model doi: 10.7243/2053-7662-5-3 [6,9] and the standard mixed-effects models [24] are the most important. A standard mixed-effects model is used predominantly to model method comparison data [2,5,25,26,30]. The validation of the mixed-effects model highly depends on assumptions such as constant error variance (homoscedasticity) and normality of error terms.…”

Section: Introductionmentioning

confidence: 99%

“…The validation of the mixed-effects model highly depends on assumptions such as constant error variance (homoscedasticity) and normality of error terms. However, these assumptions are violated in practice [2,5,30]. Basically, the error variability may change with the magnitude of measurements.…”

Section: Introductionmentioning

confidence: 99%

A statistical method for assessing agreement between two methods of heteroscedastic clinical measurements using the copula method

Aravind¹,

Nawarathna²

2017

J Med Stat Inform

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Background: A method comparison study is a topic of considerable interest in health and biomedical-related fields. The use of this study is to compare a new method with a standard established method. There are two typical steps in method comparison studies, namely modeling the data set and using the fitted model to analyze method comparison data. Methods: As a usual practice to model method comparison data, many recommend a mixed-effects model which assumes constant error variance (homoscedasticity) and normality of error terms. However, these assumptions are generally violated in practice. Thus, in this study, our main goal is to propose a copula based model to deal with non-replicated method comparison data. Moreover, a simulation procedure is carried out to validate the proposed model by means of different copula models. Results: Results indicate that the Normal and Gumbel copulas give more accurate results in terms of bias, mean-squared error and coverage probability values of estimators. Further it is confirmed that the accuracy of model increases with the Kendall tau (τ) correlation between methods and the number of observations. Furthermore, the proposed methodology is illustrated by analyzing finger and arm systolic blood pressure data. Besides the Total Deviation Index (TDI) and Concordance Correlation Coefficient (CCC) values are used to check the agreement between the two methods. Conclusion:The proposed model based on the Copula method can be used to model the method comparison data with balanced and unbalanced data designs.

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Estimating the Generalized Concordance Correlation Coefficient through Variance Components

Cited by 207 publications

References 17 publications

Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness

Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness

Assessing inter-rater agreement between multiple medical instruments with heteroscedastic measurements

A statistical method for assessing agreement between two methods of heteroscedastic clinical measurements using the copula method

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