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Weighted kappa statistic for clustered matched-pair ordinal data

Author

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  • Yang, Zhao
  • Zhou, Ming

Abstract

As an important extension of the regular kappa statistic, the weighted kappa statistic has been widely used to assess the agreement between two procedures for independent matched-pair ordinal data. For clustered matched-pair ordinal data, based on the delta method and sampling techniques, a non-parametric variance estimator for the weighted kappa statistic is proposed without within-cluster correlation structure or distributional assumptions. The results of an extensive Monte Carlo simulation study demonstrate that the proposed weighted kappa statistic provides consistent estimation, and the proposed variance estimator behaves reasonably well for at least a moderately large number of clusters (e.g., K≥50). Compared with the variance estimator ignoring dependence within a cluster, the proposed variance estimator performs better in maintaining the nominal coverage probability when the intra-cluster correlation is fair (ρ≥0.3), with more pronounced improvement when ρ is further increased. Moreover, under the general analysis of variance setting with systematic variability between procedures and clusters being included as a component of total variation, the equivalence between weighted kappa statistic and intraclass correlation coefficient is established. To illustrate the practical application of the proposed estimator, two real medical research data examples of clustered matched-pair ordinal data are analyzed, including an agreement study to compare two methods for assessing cervical ectopy, and a physician–patients data example from the Enhancing Communication and HIV Outcomes study.

Suggested Citation

  • Yang, Zhao & Zhou, Ming, 2015. "Weighted kappa statistic for clustered matched-pair ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 1-18.
    • Handle: RePEc:eee:csdana:v:82:y:2015:i:c:p:1-18
    DOI: 10.1016/j.csda.2014.08.004
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    References listed on IDEAS

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    1. Jun-Mo Nam, 2000. "Interval Estimation of the Kappa Coefficient with Binary Classification and an Equal Marginal Probability Model," Biometrics, The International Biometric Society, vol. 56(2), pages 583-585, June.
    2. Huiman X. Barnhart & John M. Williamson, 2002. "Weighted Least-Squares Approach for Comparing Correlated Kappa," Biometrics, The International Biometric Society, vol. 58(4), pages 1012-1019, December.
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    Cited by:

    1. Sandipta Debanshi & Swades Pal, 2020. "Assessing gully erosion susceptibility in Mayurakshi river basin of eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 883-914, February.
    2. Yang, Zhao & Zhou, Ming, 2015. "Kappa statistic for clustered physician–patients polytomous data," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 1-17.
    3. Matthijs J. Warrens, 2021. "Kappa coefficients for dichotomous-nominal classifications," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(1), pages 193-208, March.
    4. Matthijs J. Warrens & Bunga C. Pratiwi, 2016. "Kappa Coefficients for Circular Classifications," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 507-522, October.

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