Global hypothesis test to compare the likelihood ratios of multiple binary diagnostic tests with ignorable missing data

Abstract

In this article, a global hypothesis test is studied to simultaneously compare the likelihood ratios of multiple binary diagnostic tests when in the presence of partial disease verification the missing data mechanism is ignorable. The hypothesis test is based on the chi-squared distribution. Simulation experiments were carried out to study the type I error and the power of the global hypothesis test when comparing the likelihood ratios of two and three diagnostic tests respectively. The results obtained were applied to the diagnosis of coronary stenosis.