Sentence examples for multivariate analyses multiple from inspiring English sources

In multivariate analyses, multiple linear regressions were performed to analyze the relationship between factor solutions of HADS and CVC.

In the multivariate analyses, multiple traits (waist circumference, OGIS, beta cell glucose sensitivity, insulinogenic index, ISR [0 30] and ISR [30 120]) were analysed simultaneously.

To decrease confounding bias, we performed multivariate analyses: multiple regressions were carried out using a stepwise backward method, after ensuring sample adequacy, linearity of the model, residual normality and non-collinearity of retained items (variance inflation factor <2).

We have used hypothesis-based cDNA arrays and quantitative PCR to quantify the expression of selected sets of genes followed by multivariate analyses in multiple independent samples.

Additionally, the sample size was limited for analysis across time, preventing multivariate analyses with multiple covariates.

Multivariate analyses using multiple linear regression (forward-stepwise selection) were performed to determine variables potentially linked to medium term QOL levels.

As it is based on multivariate analyses, the multiple DPCoA in its three forms can be used to analyze large data sets.

Multivariate analyses using multiple correspondence analysis (MCA) and ascendant hierarchical clustering on clinical, biological and therapeutic characteristics of SHPT were performed to identify subgroups of patients [ 22].

For the bivariate associations Pearson correlations were used, for the multivariate analyses several multiple regression analyses were computed (all predictors were entered simultaneously and remained in the final model).

In multivariate analyses with multiple patient and treatment factors, only SCORE at baseline, and addition of or dose change in lipid lowering or antihypertensive medications over the course of the study were significantly related to change in SCORE.

To obtain a preliminary architecture of the data, we conducted classical descriptive multivariate analyses using multiple correspondence analysis (MCA), clustering and principal component analysis (PCA) [17] as a first step to evaluate the data structure, reveal unknown relationships and reveal clusters of genes potentially involved in immune responses.