Multivariate and Multiple Contrast Testing in General Covariate-adjusted Factorial Designs

TL;DR

This paper develops a flexible multivariate contrast testing method within a covariate-adjusted framework, enabling analysis of complex intervention effects across multiple correlated outcomes in various scientific fields.

Contribution

It introduces an extension of multiple contrast testing procedures within a semiparametric MANCOVA framework that relaxes traditional assumptions like normality and homoscedasticity.

Findings

Effective analysis of multivariate intervention data demonstrated

Method handles non-normal and heteroscedastic data

Application to psychological and physiological outcomes

Abstract

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 (MCTPs) capable of handling multivariate outcomes and covariates. Given this background, we propose an extension of MCTPs within a semiparametric MANCOVA framework that allows applicability beyond multivariate normality, homoscedasticity, or non-singular covariance structures. We illustrate our approach by analysing multivariate psychological intervention data, evaluating joint physiological and psychological constructs such as heart rate variability.