  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.
