Speaker
Description
Analysis of covariance (ANCOVA) assesses the effect of a group factor on a response while accounting for covariate information. We propose a nonparametric ANCOVA based on Mann-Whitney effects, specifically designed for randomized trials. Unlike classical ANCOVA, our approach does not rely on distributional assumptions or metric-scale data; Ordinal measurements (such as Likert-scale items) are sufficient, as the proposed estimators are rank-based. Through theoretical derivations and extensive simulations, we demonstrate that the proposed nonparametric ANCOVA reliably controls type-I error rates across challenging scenarios - including heteroscedasticity, small samples, and imbalanced group designs. The method integrates seamlessly with modern multiple testing procedures such as multiple contrast tests and naturally extends to cases with multivariate responses.
This integration enables us to derive simultaneous confidence intervals for Mann-Whitney effecs that are substatially narrower than established intervals, which do not use covariate information.
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