Computational methods to simultaneously compare the predictive values of two diagnostic tests with missing data: EM-SEM algorithms and multiple imputation
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 predicti...
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| Published in | Journal of statistical computation and simulation Vol. 91; no. 16; pp. 3358 - 3384 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
02.11.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0094-9655 1563-5163 |
| DOI | 10.1080/00949655.2021.1926461 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Roldán-Nofuentes, J. A. |
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| Cites_doi | 10.1111/1467-9876.00102 10.1080/01621459.1991.10475152 10.2307/2530820 10.1111/j.2517-6161.1977.tb01600.x 10.1002/9780470906514 10.1002/sim.1066 10.1093/oso/9780198509844.001.0001 10.1201/9781439821862 10.1080/10629360600938102 10.1002/(SICI)1234-988X(199610)6:3<129::AID-MPR164>3.3.CO;2-A 10.1093/biomet/79.1.103 10.1002/9780470316696 10.1002/sim.2332 10.1002/9781119013563 10.1016/j.csda.2011.06.003 10.1177/0962280216634755 10.1016/j.jspi.2007.03.054 10.1002/sim.4067 10.1002/sim.5587 10.1111/j.0006-341X.2000.00345.x 10.1080/01621459.1991.10475130 10.1002/sim.2715 |
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| SubjectTerms | 6207 EM and SEM algorithms missing data multiple imputation partial verification predictive values |
| Title | Computational methods to simultaneously compare the predictive values of two diagnostic tests with missing data: EM-SEM algorithms and multiple imputation |
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