ArticleJournal of biopharmaceutical statistics2026
Multivariate equivalence and component-wise superiority tests for paired samples.
Article in Journal of biopharmaceutical statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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Abstract
Most existing multivariate equivalence and superiority tests have been developed for two-sample settings, while methods for one-sample settings remain scarce. Moreover, most multivariate superiority tests combine superiority and non-inferiority tests rather than providing a purely one-sided test of joint superiority. To fill these gaps, we adapt the existing Intersection-Union Test and propose Likelihood Ratio Nonparametric and Parametric Bootstrap Tests for paired multivariate equivalence and component-wise superiority testing. Simulation studies demonstrate the ability of the proposed approach to detect equivalence and component-wise superiority while maintaining control of type I error rates. An application to real data further illustrates the practical utility of the approach. Overall, combining classical likelihood methods with bootstrap resampling yields a powerful and flexible solution for paired multivariate equivalence and component-wise superiority testing.
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