Statistical Analysis
REGRESSION, REMISSION
Statistical analysis was performed from February 1 to June 15, 2022. Initial descriptive analysis was conducted to characterize the randomized groups at baseline on main demographic characteristics and clinical measures. No statistical tests were used to compare groups. The main analysis followed the intention-to-treat principle. For the primary hypothesis, the MADRS scores at week 12 were compared between groups using a mixed-effect model. The outcome for the model was postbaseline MADRS scores, and the fixed effects included baseline MADRS scores, treatment group, randomization site, time (postbaseline time points), and a treatment group × time point interaction. To account for the dependencies between repeated measures on the same participants, an intercept for each participant was included as a random effect.A linear contrast was used to test the primary hypothesis: the baseline adjusted difference in MADRS scores between groups at week 12. Two-tailed tests and an α = .05 were used for significance. All estimates are presented with 95% CIs. To estimate ES, standardized regression coefficients were calculated using the overall SD of the baseline outcome scores as the metric.Secondary analyses used similar mixed models, with baseline adjusted with time, treatment group, and the treatment group × time interaction as independent variables. As for the primary outcome, the main time point of interest was 12-week follow-up, and treatment effects were determined by contrasts at the measured time point. To compare remission rates between groups, logistic regression was used in which participants were classified as being in remission or not. Outcomes at time points other than 12-week follow-up were considered exploratory, and the focus was ES (ie, standardized regression coefficients).An exploratory analysis was conducted to assess whether baseline BMI, CRP, or lipids moderated response to simvastatin by adding each baseline measure to a model for the primary outcome at 12 weeks, adjusted for baseline MADRS scores and including a treatment group × baseline BMI, CRP, and lipids interaction. Lipids, BMI, and CRP were considered moderators if the interaction with treatment group was significant (at a significance level of α = .05, using bootstrapping for inference and construction of CIs), in which case exploratory plots that assess the group effect at different levels of the moderators were used to study the nature of the moderation.To assess whether changes in peripheral levels of CRP and lipids mediate the effect of the intervention on the change in MADRS scores, an initial descriptive analysis was conducted to assess bivariate association between treatment group and change in CRP and lipids and between change in CRP and lipids (at 12 weeks) and change in MADRS scores. A mediation model was then fitted to the data that jointly modeled the effects of treatment on the change in CRP and lipids at 12 weeks and on MADRS scores at 12 weeks. The mediation effect was tested by the proportion of the total effect from treatment group to change in MADRS attributable to changes in CRP and lipids. This model was fitted in Lavaan in R software, version 4.1.2 (R Foundation for Statistical Computing) and the indirect effect estimated through bootstrap resampling.To compare the frequency of AEs, we visualized percentage risk and relative risk ratios for each AE and present them by treatment group. To handle missing data, we identified baseline factors associated with missingness using logistic regression with the outcome (missing or not). Two variables were associated with missingness and were thus included in the analysis models, namely years of education and time since first diagnosis in months. Mixed-effect models fitted through maximum likelihood were conducted, assuming the data to be missing at random.