2.15. Statistical analysis
REGRESSION, SECONDARY, REGRESSION
Data was entered into a database built with the Statistical Package for the Social Sciences®, version 21.0. The analysis plan was finalized and approved prior to the data lock for the final analysis. Quantitative variables were described as means (standard deviation), and non-normal quantitative variables were summarized by median (IQR). Qualitative variables were described with measures of absolute and percentage frequency. To compare intervention and control groups in relation to qualitative variables, Pearson’s chi-square test or Fischer’s exact test was used, when recommended. The comparison of quantitative variables between groups was performed by applying the Student’s t test for unpaired samples or ANOVA, if indicated, the non-parametric Mann-Whitney test or Kruskal Wallis test.The occurrence of the primary study outcome (adherence) was calculated by the ratio between the total number of participants who achieved adherence to treatment and the total number of participants in the group, multiplied by 100, calculated separately for the intervention and comparative groups, in times 0 and 6 months. The calculation was carried out either considering maximum adherence (those at baseline who had low or moderate adherence and after 6 months they became adherent) or improvement in adherence (those who at baseline had low adherence and after 6 months became adherent or reached moderate adherence, as well as who in the baseline had moderate adherence and after 6 months became adherent).For primary outcomes, the relative risk with the respective 95% CI was used as a measure of effectiveness, obtained from the ratio between the adherence of the intervention group and the adherence of the comparative group, at times 0 and 6 months after application of the intervention program. For adjustments for potential confounders, Logistic Regression was used to estimate the adjusted ORs with the respective 95% CI. Associations with p values less than 0.05 were considered significant. The analysis was made per protocol.The analysis of QoL (secondary outcome) was performed by comparing the mean differences of the 8 domains of the SF-36 score between Visit 2 and Visit 1 with the paired t-test calculated, separately for the CG and IG. The variables that showed differences of p <0.20 were included in the construction of the regression models. For adjustments for potential confounders, Multiple Linear Regression was used to adjust the effect of the intervention by confounding variables.