Sentence examples for baseline variables were analysed from inspiring English sources

Baseline variables were analysed as predictors of "relapse".

Baseline variables were analysed by dropout status with t test, Kruskal Wallis test or χ test for symmetrical, skewed or categorical variables correspondingly.

The proportions of the following baseline variables were analysed at age 40: gender, presence of VESC, disc contour, disc degeneration, spondylolisthesis, LBP, heavy smoking, heavy physical workload, Body Mass Index, highest educational level, employment status, and the Back Beliefs Questionnaire score.

Baseline prognostic variables were analysed using logistic regression with MSBQ ≥ 5 as the primary dependent variable and SBI ≥ 7 as the secondary dependent variable.

The changes from baseline in efficacy variables were analysed using an ancova model, with treatment and pooled centre as the classification factor and baseline as the covariate.

Changes from baseline in categorical variables were analysed using the CMH method stratified by country.

Baseline demographic and clinical variables were analysed by Chi-square or Student's t-Test.

First, the association between baseline BMI and the outcome variables were analysed adjusting for age and gender.

Differences between groups for patient characteristics at baseline for nominal and ordinal variables were analysed by χ tests.

A number of sociodemographic, functional and health-related variables were analysed as possible confounders.Results For those with no depressive disorders at baseline, the only baseline variable significantly associated with depressive disorders five years later was the PGCMS score.

Differences in binary prevalence variables were analysed as the odds ratio between post-implementation and baseline.