Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data Roldán Nofuentes, José Antonio Bouh Regad, Saad EM algorithm Partial verification SEM algorithm 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 Alzheimerʹs disease. 2021-11-09T07:40:07Z 2021-11-09T07:40:07Z 2021 info:eu-repo/semantics/article Roldán‐Nofuentes, J.A.; Regad, S.B. Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data. Mathematics 2021, 9, 2834. https://doi.org/10.3390/ math9212834 http://hdl.handle.net/10481/71377 10.3390/math9212834 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI