Computational methods to simultaneously compare the predictive values of two diagnostic tests with missing data: EM-SEM algorithms and multiple imputation

• Published in: Journal of statistical computation and simulation Vol. 91; no. 16; pp. 3358 - 3384
• Main Author: Roldán-Nofuentes, J. A.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 02.11.2021
• Subjects: 6207; EM and SEM algorithms; missing data; multiple imputation; partial verification; predictive values
• ISSN: 0094-9655; 1563-5163
• DOI: 10.1080/00949655.2021.1926461

Predictive values are measures of the clinical accuracy of a binary diagnostic test, and depend on the sensitivity and the specificity of the diagnostic test and on the disease prevalence among the population being studied. This article studies hypothesis tests to simultaneously compare the predictive values of two binary diagnostic tests in the presence of missing data. The hypothesis tests were solved applying two computational methods: the expectation maximization and the supplemented expectation maximization algorithms, and multiple imputation. Simulation experiments were carried out to study the sizes and the powers of the hypothesis tests, giving some general rules of application. Two R programmes were written to apply each method, and they are available as supplementary material for the manuscript. The results were applied to the diagnosis of Alzheimer's disease.