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      Sklar's Omega: A Gaussian Copula-Based Framework for Assessing Agreement

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          Abstract

          The statistical measurement of agreement is important in a number of fields, e.g., content analysis, education, computational linguistics, biomedical imaging. We propose Sklar's Omega, a Gaussian copula-based framework for measuring intra-coder, inter-coder, and inter-method agreement as well as agreement relative to a gold standard. We demonstrate the efficacy and advantages of our approach by applying it to both simulated and experimentally observed datasets, including data from two medical imaging studies. Application of our proposed methodology is supported by our open-source R package, sklarsomega, which is available for download from the Comprehensive R Archive Network.

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          Measurement in Medicine: The Analysis of Method Comparison Studies

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            R: A Language for Data Analysis and Graphics

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              Correlated binary regression with covariates specific to each binary observation.

              Regression methods are considered for the analysis of correlated binary data when each binary observation may have its own covariates. It is argued that binary response models that condition on some or all binary responses in a given "block" are useful for studying certain types of dependencies, but not for the estimation of marginal response probabilities or pairwise correlations. Fully parametric approaches to these latter problems appear to be unduly complicated except in such special cases as the analysis of paired binary data. Hence, a generalized estimating equation approach is advocated for inference on response probabilities and correlations. Illustrations involving both small and large block sizes are provided.
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                Author and article information

                Journal
                07 March 2018
                Article
                1803.02734
                8f640d9e-8836-463a-8a34-1696f498835c

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                stat.ME

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