A statistical multivariate test using the Principal Component Analysis (PCA) method which allows highlighting groups of close ions from dense and complex data sets was conducted to compare the molecular data of Cuvierian tubules from stressed and relaxed holothuroids.
Many of these variables remained associated with VRE acquisition in multivariate testing using generalized linear mixed models (Table 3).
Multivariate testing using the proportional hazards model of Cox was applied to test for independent predictors of survival in adjustment with the clinical covariates age, gender, and disease stage (M category).
Our procedure involves parametric multivariate outlier testing using a multivariate Gaussian model, which we apply to principal components of the quality measure matrix.
The validation tests using multivariate analyses were developed with the data of the randomized placebo-controlled clinical trial of intralesional rhEGF in advanced DFUs (16).
Univariate and multivariate tests were used to compare the characteristics and outcomes of those with positive and negative BAC.
Mauchly's test was consulted to assess the Sphericity of the data; if the assumption of Sphericity was violated then Wilks' Lambda multivariate tests were used, otherwise Sphericity was assumed.
The normality of multivariate data was tested using probability plot of PCA model.
Variables with a skewed distribution were transformed using quadratic or Log transformations and multivariate normality was tested using Henze-Zirkler's test [56].
Multivariate discrimination was tested using a support vector machine [32], with testing and training sets selected using holdout cross-validation with half the data allocated to each training set.
