Abstract
Evaluation of agreement among multiple methods of clinical measurement is a topic of considerable interest in health sciences. As in an analysis of variance comparing more than two treatment means, when more than two measurement methods are compared, performing multiple comparisons and ranking pairs of methods on the basis of their extent of agreement are of primary concern. This article develops frequentist and Bayesian methodologies for this purpose. In particular, simultaneous confidence bounds and simultaneous credible bounds are developed for multiple comparisons. Moreover, two approaches are described for ranking method pairs—one based on simultaneous bounds and the other based on posterior probabilities of possible orderings. The proposed methodologies can be used with any scalar measure of agreement. Their small-sample performance is evaluated using simulation. Extension of the basic methodologies to incorporate covariates is illustrated using a blood pressure dataset.
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