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For bivariate analyses, we compared weighted proportions using the Wald test.
These results are presented in table 2. Using bivariate analyses, we compared female doctors with male doctors.
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For our bivariate analyses, we divided our sample at age 40 to compare younger versus middle-aged adults.
For bivariate analyses, we used linear regression.
For bivariate analyses, we used χ tests for categorical variables.
In bivariate analyses, we examined the relationship between phthalate and phenol biomarkers and maternal characteristics.
In bivariate analyses, when compared to urban residents, rural residents were younger, more likely to be uninsured, reported significantly higher levels of disability, and reported more depression/sadness.
Bivariate analyses also compared 24-month levels of Th1, Th2, and CD4 according to various environmental exposures with two-tailed t-tests and analysis of variance with post hoc tests using Bonferroni adjustment.
We conducted bivariate analyses to compare participant characteristics between the two patient groups.
We conducted bivariate analyses to compare patient characteristics among those who intensified therapy and those who stayed with the same therapy using t tests for continuous variables and χ tests for categorical variables.
In bivariate analyses, medians were compared using Wilcoxon rank-sum and sign-rank tests; proportions were compared using chi-square tests.
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