Hypothesis tests for multiple responses regression: effect of probiotics on addiction and binge eating disorder
Lineu Alberto Cavazani de Freitas, Ligia de Oliveira Carlos, Ant\^onio, Carlos Ligocki Campos, Wagner Hugo Bonat

TL;DR
This paper introduces a Wald statistic-based hypothesis testing framework for multivariate covariance generalized linear models, applicable to complex clinical trial data with multiple responses, and demonstrates its effectiveness through simulations and real data analysis.
Contribution
It develops a novel testing strategy for McGLMs, enabling hypothesis testing on regression and dispersion parameters in multivariate non-Gaussian data.
Findings
Proposed tests maintain confidence levels close to nominal in simulations.
Application to clinical trial data shows probiotics reduce addiction and binge eating over time.
No significant difference between treatment and placebo groups at individual time points.
Abstract
Clinical trials are common in medical research where multiple non-Gaussian responses and time-dependent observations are frequent. The analysis of data from these studies requires statistical modeling techniques that take these characteristics into account. We propose a general strategy based on the Wald statistics to perform hypothesis tests like ANOVAs, MANOVAs and multiple comparison tests on regression and dispersion parameters of multivariate covariance generalized linear models (McGLMs). McGLMs provide a general statistical modeling framework for normal and non-normal multivariate data analysis along with a wide range of correlation structures. We design different simulation scenarios to verify the properties of the proposed tests. The results are promising showing that the proposed tests present the levels of confidence close to the specified one for all simulation study…
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Taxonomy
TopicsSensory Analysis and Statistical Methods · Animal Nutrition and Physiology · Meat and Animal Product Quality
