Multivariate and multiple contrast testing in general covariate-adjusted factorial designs
提出一种半参数多元协方差分析框架下的多重对比检验方法，适用于非正态、异方差或奇异协方差结构的数据，帮助研究者同时识别全局和局部干预效应。
Evaluating intervention effects on multiple outcomes is a central research goal in a wide range of quantitative sciences. It is thereby common to compare interventions among each other and with a control across several, potentially highly correlated, outcome variables. In this context, researchers are interested in identifying effects at both, the global level (across all outcome variables) and the local level (for specific variables). At the same time, potential confounding must be accounted for. This leads to the need for powerful multiple contrast testing procedures ( mctp s) capable of handling multivariate outcomes and covariates. Given this background, we propose an extension of mctp s within a semiparametric mancova framework that allows applicability beyond multivariate normality, homoscedasticity, or non-singular covariance structures. To realise this, we implement a generalised resampling-based method for the determination of critical values. We illustrate our approach by analysing multivariate psychological intervention data, evaluating joint physiological and psychological constructs such as heart rate variability.