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Measuring Agreement of Multivariate Discrete Survival Times Using a Modified Weighted Kappa Coefficient

Authors

  • Ying Guo,

    Corresponding author
    1. Department of Biostatistics and Bioinformatics, Rollins School of Public Health of Emory University, 1518 Clifton RD, Atlanta, Georgia 30322, U.S.A.
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  • Amita K. Manatunga

    1. Department of Biostatistics and Bioinformatics, Rollins School of Public Health of Emory University, 1518 Clifton RD, Atlanta, Georgia 30322, U.S.A.
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email: yguo2@sph.emory.edu

Abstract

Summary Assessing agreement is often of interest in clinical studies to evaluate the similarity of measurements produced by different raters or methods on the same subjects. We present a modified weighted kappa coefficient to measure agreement between bivariate discrete survival times. The proposed kappa coefficient accommodates censoring by redistributing the mass of censored observations within the grid where the unobserved events may potentially happen. A generalized modified weighted kappa is proposed for multivariate discrete survival times. We estimate the modified kappa coefficients nonparametrically through a multivariate survival function estimator. The asymptotic properties of the kappa estimators are established and the performance of the estimators are examined through simulation studies of bivariate and trivariate survival times. We illustrate the application of the modified kappa coefficient in the presence of censored observations with data from a prostate cancer study.

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