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...
A common goal in medical research is to estimate a difference between treatment groups and quantify its uncertainty, or to infer a population-level difference. The most commonly used nonparametric group difference measure is the Mann-Whitney (MW) effect. It applies to a broad range of outcomes, including skewed, heteroskedastic and ordinal distributions, since it does not assume a parametric...
In the context of a two-group comparison, when the assumption of equal variances between groups is doubtful or the data may be skewed or ordinal, the classical t-test and an effect measure parameterized in terms of means may no longer be suitable. In such cases, it appears more appropriate to formulate the problem as the nonparametric Behrens-Fisher problem of testing H0: θ = 1/2, where θ =...
Feedback is pervasive in biological and biomedical systems, yet many causal discovery methods, including widely used score-based approaches such as NOTEARS, impose acyclicity and may therefore misrepresent gene regulatory, pharmacological, or cellular processes. Building on recent advances in cyclic causal inference, such as the intervention-capable Bicycle method, we investigate how...
Background
Longitudinal observational data frequently involve time-varying confounding, autoregressive dependence, and potential reciprocal feedback between processes. These features complicate the estimation of cross-lagged causal effects and challenge the assumptions underlying standard modelling approaches. Methodological evaluation requires transparent, reproducible simulation frameworks...
