Quantitative data
REGRESSION, SECONDARY
Analyses will follow intent-to-treat principles, with participants analyzed according to randomized treatment assignment of the cluster in which they reside. Most analyses will use the individual participant as the unit of analysis, but because randomization is at the cluster level, model-based approaches that account for the correlation between individual participants within a cluster will be used.Collected data will be summarized in tabular and graphic forms. Demographics and other study variables will be summarized by treatment groups. Mean (SD) or median (interquartile), as appropriate, will be used to describe numerical data, and frequency and percentage will be used to describe the distribution of categorical variables between treatment groups. Two-sample t-test, Mann–Whitney U test, chi-square test, and Fisher’s exact test will be used to compare the distribution of study variables between two-intervention groups as appropriate. Longitudinally collected data will be summarized by intervention groups and data collection time points.Analysis of variance (ANOVA) and generalized linear and logistic regression models will be used to analyze outcomes measured at one time point, as appropriate. Trajectories of change for outcomes that are repeatedly measured over time will be examined using generalized linear mixed models (GLMMs) and linear, logistic, and negative binomial/Poisson regression models for continuous, binary, and count outcomes. Total scores and subscales from assessments will be used when applicable. The use of GLMM models will allow for an estimation of between-group differences in outcomes at each time point of measurement and will allow for an estimation of changes over time and time by group interactions while accounting for within-subject associations arising from the repeated measures. Clustering of participants will be accounted for in the GLMMs models by including cluster as a random or fixed effect and in the generalized models by using robust variance estimates. Potential covariates will be selected based on consideration of study arm balance and potential confounders for an outcome previously reported in the literature.The analyses described above will be repeated for the secondary outcomes. We will construct GLMMs for repeated outcomes (e.g., mixed-effects linear regression models for continuous outcomes and mixed-effects logistic regression models for dichotomous outcomes) and ANOVA and generalized linear or logistic regression models for outcomes measured at one time point.All analyses will be two-tailed with an overall level of significance of 0.05. The Benjamini–Hochberg method will be used for adjusting the level of significance for multiple testing. The statistical software SAS, version 9.4, and the latest version of R will be used for data analysis.