Sentence examples for bivariate analysis were introduced from inspiring English sources

In order to identify independent risk markers of anemia at PICU discharge, we performed multivariable GLMM analysis using the following steps: First, the variables with a p value less than 0.2 in bivariate analysis were introduced in a multivariable GLMM.

Variables shown in Table  1 showing P values < 0.20 in the bivariate analysis were introduced in the model.

Significant categorical and continuous variables (QLQ-C30 scores) by bivariate analysis, were introduced into the multivariate models using analysis of variance (ANOVA) for each scale score of the OUT-PATSAT35 questionnaire.

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].

Variables with P-values <0.2 on bivariate analysis were then introduced into the multivariate model [ 18].

Descriptive statistics and bivariate analysis were used to assess the data.

All variables with p-values < = 0.25 in bivariate analysis were included in our multivariate model.

Univariate and bivariate analysis were performed as appropriate.

Covariates significant at p < 0.05 in bivariate analysis were individually incorporated, ordered by strength of the bivariate correlation.

Before a systematic regression analysis, some pieces of bivariate analysis are offered.

A bivariate analysis was performed, using Chi2, ROC curve and relative risk.