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

José Antonio Roldán-Nofuentes and Saad Bouh Regad
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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

Mathematics, 2021, vol. 9, issue 21, 1-24

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.

Keywords: EM algorithm; partial verification; SEM algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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