Statistical analysis
UC
SECONDARY
Assuming an average cluster size of 15 participants, an allocation ratio of 2:1 (control:intervention), a coefficient of variation of cluster size of 0.4 (cluster size ranging from 5 to 30), and an intra-cluster correlation coefficient of 0.01, we estimated that with 600 participants [195 intervention arm (13 clusters), 405 control arm (27 clusters)] the trial would have 89% power to detect a difference of 0.30 standard deviations (We tested the null hypothesis of no difference in mean HoNOSCA score between the MT and UC groups at month 15 post-randomisation, using a generalised linear mixed effect model (GLMM) with random and fixed effects (to allow for the hierarchical structure of the data and adjust for trial arm, time point and baseline characteristics, HoNOSCA score, gender and diagnosis). Secondary outcomes HoNOSCA-SR and quality of life (WHOQOL-BREF) were analysed using a hierarchical linear mixed model with random effects for the four levels (country, site, participant and follow-up time) and adjustment for the same fixed effects. For other secondary outcomes, descriptive analyses were conducted. The significance level was set at 5%, with no adjustment for multiple testing. All analyses were on an intention-to-treat basis and followed a pre-specified statistical analysis plan (available at To examine the cost of implementing the intervention, clinicians were invited to complete a questionnaire on the time burden of completing TRAM. Time spent by the Warwick team preparing and checking TRAM reports was also recorded. We also captured the resources required to set up the intervention and conduct initial training of clinicians. Resource use data were combined with unit cost data (see online Supplementary appendix, ‘Intervention costing’) to estimate intervention costs.All analyses were conducted using Stata 16 (StataCorp, 