Statistical analyses
depression
REGRESSION
The primary feasibility outcome (#1, ‘Number of patients lost to follow-up’) was summarised with frequencies and percentages with exact 95% CIs (Clopper-Pearson methodSeveral continuous outcomes (#8–9; clinical outcomes #13–14, #16–17, #20) were analysed using linear regression models where the dependent variable was the follow-up score and each model included (a) a dummy variable representing treatment allocation (1=‘Decision tool’; 0=‘TAU’) and (b) the baseline score. To account for the clustering of patients within GP practices, SEs were adjusted using a sandwich estimatorFor individual scales, we used published guidance on how to handle missing items. Where such guidance was not available, scales were pro-rated for individuals where 20% or less of items were missing. FIBSER scores were summarised based on item three (‘Burden’: ‘In the past week, how much have the side effects to your medications for depression interfered with your day-to-day activities?’) of the scale (see No participants were missing information at baseline, therefore, no imputation was carried out. Participants with missing follow-up information were excluded from regression models. No sensitivity analyses or subgroup analyses were performed. 95% CIs were treated as underpowered and not used as the basis for inferential conclusions. P values were not presented for any analyses. Analyses were conducted using R V.4.0.4.