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Bivariate analysis was used to compare demographic factors and outcomes among subjects with and without prior criminal charges.
Bivariate analysis was used, according to the quantitative or qualitative nature of the variables (Chi-square, t student, Fisher's Exact Test, Mann-Whitney U, Correlation coefficients of Pearson or Spearman) and according to normality criteria (Kolmogorov Smirnov Z).
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).
Bivariate analysis was used to identify the factors associated with labor force participation.
Bivariate analysis was used primarily to check which variables had association with the dependent variable individually.
Bivariate analysis was used to further explore the association between isolation and a range of variables.
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Descriptive statistics and bivariate analysis were used to assess the data.
Descriptive statistics and bivariate analysis were used to characterize the study sample.
In a multivariate general linear modeling analysis, both operational definitions of multimorbidity were used successively as dependent variable, and the variables with a significant relationship with multimorbidity in the bivariate analysis were used as potential predictors in the model.
Independent variables that showed marginal associations (p < 0.20) in the bivariate analysis were used in a logistic regression analysis in order to identify independent predictors of both patient delay > 30 days and health system delay > 15 days.
Survival was used as independent variable, and certain dimensions on the SF-36 and QLQ C-30 that were statistically significant on bivariate analysis were used as candidate predictors as explained below.
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