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In medical device comparison studies, equivalency test is commonly used todemonstrate two measurement methods agree up to a pre-specified performancegoal based on the paired repeated measures. Such equivalency test ofteninvolves controlling the absolute differences that depend on both the mean andvariance parameters, and poses some challenges for statistical analysis. Forexample, for the oximetry comparison study that motivates our research, FDA hasclear guidelines approving an investigational pulse oximeter in comparison to astandard oximeter via testing the root mean squares (RMS), a composite measureof both mean and variance parameters. For the hypothesis testing of thiscomposite measure, existing methods have been either exploratory or relying onthe large-sample normal approximation with conservative and unsatisfactoryperformance. We develop a novel generalized pivotal test to rigorously andaccurately test the system equivalency based on RMS. The proposed method haswell-controlled type I error and favorable performance in our extensivenumerical studies. When analyzing data from an oximetry comparison study,aiming to demonstrate performance equivalency between an FDA-cleared oximetrysystem and an investigational system, our proposed method resulted in a highlysignificant test result strongly supporting the system equivalency. We alsoprovide efficient R programs for the proposed method in a publicly available Rpackage. Considering that many practical equivalency studies of diagnosticdevices are of small to medium sizes, our proposed method and software timelybridge an existing gap in the field.

Efficient and powerful equivalency test on combined mean and variance with application to diagnostic device comparison studies