Sentence examples for multivariate model when from inspiring English sources

Potential confounders were selected based on a univariate analysis (p < 0.20) and were retained in the multivariate model when they changed the beta-coefficient with more than 10%.

Factors that were associated with these outcomes at a p value ≤0.1 were included in the multivariate model; when there was a high collinearity between factors (correlation coefficient >0.4), only one factor was entered into the model.

Weight loss dropped out of the multivariate model when performance status was also included.

Potential predictors were included in the multivariate model when they were retained after backward stepwise elimination.

p-Values are reported from the final multivariate model when effects were adjusted for other factors.

We were not able to model case status with a multivariate model when using backwards hierarchical elimination.

The study of multiple effects of a number of variables, and the assessment of the corresponding environmental risks, may require the adoption of suitable multivariate models when the variables at play are dependent, as it often happens in environmental studies.

We excluded variables from multivariate models when the regression coefficient for groups did not change (data available from authors).

Homocysteine was strongly related to MMA% in univariate analysis, but the association became weaker in multivariate modeling when folate was added (Table 4).

Systematic differences in the abundance of metabolite content were visualised in multivariate models when many possibly inter-related metabolites in the samples differed between the groups.

These differences disappeared in multivariate models, when important covariates were considered; otherwise, their explained variance increased in the multivariate models about a factor between 3 and 10 (see Table  3).