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Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data

Author

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  • José Antonio Roldán-Nofuentes

    (Department of Statistics, School of Medicine, University of Granada, 18016 Granada, Spain)

  • Saad Bouh Regad

    (Epidemiology and Public Health Research Unit and URMCD, School of Medicine, University of Nouakchott Alaasriya, Nouakchott BP 880, Mauritania)

Abstract

The average kappa coefficient of a binary diagnostic test is a parameter that measures the average beyond-chance agreement between the diagnostic test and the gold standard. This parameter depends on the accuracy of the diagnostic test and also on the disease prevalence. This article studies the comparison of the average kappa coefficients of two binary diagnostic tests when the gold standard is not applied to all individuals in a random sample. In this situation, known as partial disease verification, the disease status of some individuals is a missing piece of data. Assuming that the missing data mechanism is missing at random, the comparison of the average kappa coefficients is solved by applying two computational methods: the EM algorithm and the SEM algorithm. With the EM algorithm the parameters are estimated and with the SEM algorithm their variances-covariances are estimated. Simulation experiments have been carried out to study the sizes and powers of the hypothesis tests studied, obtaining that the proposed method has good asymptotic behavior. A function has been written in R to solve the proposed problem, and the results obtained have been applied to the diagnosis of Alzheimer's disease.

Suggested Citation

  • José Antonio Roldán-Nofuentes & Saad Bouh Regad, 2021. "Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data," Mathematics, MDPI, vol. 9(21), pages 1-24, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2834-:d:674612
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    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Nofuentes, Jose Antonio Roldan & del Castillo, Juan de Dios Luna, 2006. "Comparing two binary diagnostic tests in the presence of verification bias," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1551-1564, March.
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    Cited by:

    1. Xiaowei Dong & Feng Sun & Fangchao Xu & Qi Zhang & Ran Zhou & Liang Zhang & Zhongwei Liang, 2022. "Three-Parameter Estimation Method of Multiple Hybrid Weibull Distribution Based on the EM Optimization Algorithm," Mathematics, MDPI, vol. 10(22), pages 1-17, November.

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