Non-inferiority testing in longitudinal studies involving functional data presents unique challenges due to the dynamic nature of the measurements over time. Traditional methods often fail to adequately address the complex interdependencies and temporal patterns inherent in longitudinal functional datasets. In this paper, we propose a novel approach for assessing non-inferiority in longitudinal functional data. Our method leverages advancements in functional data analysis and incorporates tailored statistical techniques to account for temporal dependencies and variability across subjects. We illustrate the application of our approach through simulated and real-world longitudinal datasets, demonstrating its efficacy in accurately determining non-inferiority while preserving statistical rigor. By addressing these challenges, our approach provides a robust framework for researchers and practitioners aiming to evaluate treatment effects or interventions in longitudinal studies with functional outcomes.

Longitudinal data analysis, Functional data analysis, Non-inferiority testing

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