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We used bivariate analysis to compare adults hospitalized with influenza with and without pneumonia.
We performed bivariate analysis to compare the clinical characteristics of patients who survived with those of patients who died.
Bivariate analysis to compare the rotavirus positive and rotavirus negative patient characteristics was done using the Chi-square test or Fisher's exact for categorical variables as appropriate and the student's t test for normal distributed continuous data.
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Results will be compared with the bivariate analysis to identify factors that lose significance or strength to others.
We also used bivariate analysis to examine associations with socioeconomic indicators (Table 3).
We used bivariate analysis to estimate odds ratios (ORs) with 95% confidence intervals (CIs).
We used analysis of variance for bivariate analysis to explore associations and potential confounders.
After bivariate analysis to test the hypotheses using Pearson correlation, we performed multiple regression analysis.
These computation times imply that for the Bayesian models presented it is computationally less demanding to run one bivariate analysis compared to two ST analyses.
This additional length of stay was determined on the basis of post hoc analysis of recent clinical trial data [ 16, 21, 22] wherein bivariate analysis was conducted to compare length of stay in patients with or without moderate or severe adverse events and in patients with first-line treatment success versus failure.
Bivariate analysis was used to compare demographic factors and outcomes among subjects with and without prior criminal charges.
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