Sentence examples for bivariate analysis were selected from inspiring English sources

All variables with P < 0.2 from bivariate analysis were selected for multivariate logistic regression.

All independent variables that had a p value below 0.20 in the bivariate analysis were selected for multivariate analysis.

Independent variables with p-values less than 0.3 in the bivariate analysis were selected for multivariate linear regression.

The independent variables with a p-value of less than 0.2 in bivariate analysis were selected for adjusted analysis in each level of the hierarchical model.

The variables that present p < 0.20 in the bivariate analysis were selected for multivariate analysis by logistic regression to establish a pain model prediction.

Factors with P-value 0.2 or less at bivariate analysis were selected for further multivariate analysis and entered into a logistic regression model.

Variables showing a p value of <0.2 after bivariate regression analysis were selected for the multivariate regression model.

First, we used a classic model in which the CT variable and all covariates (baseline characteristics and post-CT characteristics related to medical treatment in the first 24 hours) associated with mortality at a significance level of less than 0.20 in bivariate analysis were introduced and selected through a backward procedure as described by Hosmer and Lemeshow [ 14].

Univariate and bivariate analysis were performed as appropriate.

A cutoff criteria of p-value <0.2 on bivariate analysis was used to select variables for inclusion in the multivariate models comparing MDR TB and XDR TB with DS TB (models 1 and 2 respectively).

In bivariate DeFries Fulker analysis, probands were selected on the basis of scoring within the highest 5%% of the z-score distribution on a given measure; the measure used to select probands is the selection variable.