4.3. Statistical Analysis
R2015b
REGRESSION
Personal characteristics of study participants are reported as mean ± standard deviation or median (interquartile range) for continuous variables, and as absolute values (percentages) for categorical variables. Normal distribution of data was assessed via the Shapiro–Wilk test. Changes from baseline for continuous variables are expressed as deltas (i.e., values at 28 days minus values at baseline) and differences between groups were evaluated using Student’s Multivariate classification models, based on PLS–DA [PLS–DA is a classification method that exploits the advantages of the PLS algorithm for dealing with correlated variables. The PLS algorithm was originally developed for regression problems and relies on the projection of the predictor matrix, X, onto a reduced space of orthogonal latent variables, yielding a matrix of scores, T (coordinates of the samples onto the latent variables subspace):A regression model is then established between the scores T and the response, y, to be predicted: The same approach can be used for classification by using a dummy binary y coding for class belonging: the elements of y can be either 1 if the sample belongs to a category or 0 if it belongs to the other category. First, classification models were built to evaluate differences in Model validation was achieved through rDCV [To account for the repeated-measure design of the intervention study, the classification models were built using, for each participant, the difference between values at 28 days and values at baseline. Analyses were performed using in-house routines running under MATLAB R2015b environment (The MathWorks, Natick, MA, USA).