Quantitative analysis
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REGRESSION
Descriptive statistics (means, standard deviations, N’s, percentages) were used to explore baseline demographic characteristics. Cronbach’s alpha was used to evaluate the internal reliability of outcome measures at baseline.We explored the difference in change in each outcome between the EASE and ETAU groups from baseline to each follow-up using linear mixed effects regression models. Models included fixed effects of treatment arm (1 = EASE; 0 = ETAU), time (0 = T0; 1 = T1; 2 = T2), and interaction terms of treatment arm X time. Random effects included participant ID, EASE group ID (for treatment group), and family ID, which was necessary to account for the fact that there were several pairs of siblings included in the analysis. For each outcome, we present the model-predicted means and corresponding 95% confidence intervals for each treatment arm and time point. We do not present the ‘treatment effect’ or Models were each estimated three times for each outcome. First, following an intent to treat principle, we estimated models including all study participants at all timepoints. Participants were analysed according to their randomization allocation and mean imputation was used if a variable was missing or the participant missed a follow-up visit. Second, we estimated the models with participants analysed according to the group they were randomized to but did not conduct imputation; therefore, not all participants contributed follow-up data. Third, we estimated the models following imputation but only included a subset of the EASE adolescent participants who were defined as ‘treatment completers’ (defined as having completed five or more sessions). Analyses were conducted using Stata, version 15.