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Analyses included chi-square tests for bivariate analyses and hierarchical linear modeling for longitudinal analyses.
Sociodemographic variables were dichotomized for bivariate analyses.
For bivariate analyses, we selected two popular methods: FBAT-GEE [10] and FBAT-PC [9], which are implemented in the programs FBAT [24] and PBAT [25], respectively.
For bivariate analyses, chi-square tests were used and, in the case of the continuous variable, age, a t-test was used.
For bivariate analyses, we used linear regression.
For bivariate analyses, the χ test was used.
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Therefore, we used the dichotomous form of the variables for ease of interpretation, reporting percentages for the bivariate analyses and odds ratios (OR) and 95% confidence intervals (CI) for the logistic regression models.
Among those people who tested positive for HIV and were therefore eligible for ART, bivariate analyses showed no relationships between CMD or cognitive impairment and attendance for such treatment (Table 3).
The Bonferroni correction was used for all bivariate analyses, and Tukey's HSD was used for the repeated measures ANOVA (from R package Tukey HSD).
In the logistic regression models, case/referent was the dependent variable and problems at baseline and problems at follow-up were independent variables, separately for the bivariate analyses (Crude OR) and combined in the same model for the multivariate analyses (Adjusted OR).
In addition, the primary alloantigen response provided a fortuitously optimal dynamic range of response for the bivariate analyses of CCL4 synthesis and HIV-1 infection monitored by intracellular CCL4 and p24 expression.
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