Statistical analysis
anomaly, depression, anxiety
REGRESSION, SECONDARY, EVENT
The sample size of 430 participants (240 in the intervention arm and in the 190 control arm) was calculated to achieve 90% power to detect a difference of 1.7 points on the HADS-A subscale and 1.5 points on the HADS-D subscale at the 2.5% significance level, allowing for 20% dropout and assuming standard deviations of 4.2 for anxiety and 3.6 for depression, a therapist intraclass correlation coefficient of 0.01 and mean cluster size of 24 at randomisation [The primary analysis was by intention to treat assuming outcomes were missing at random. We explored the robustness of this assumption through sensitivity analyses. A secondary analysis estimated the complier average causal effect (CACE) (for more details, see the statistical analysis plan in the We intended the analyses of smoking status at 6 and 12 months to work in a similar way using a mixed logistic regression model. The statistical analysis plan prespecified a strategy of successive steps for simplifying analyses in the event that there were problems with model convergence. The final analyses of smoking status used separate models for 6- and 12-month smoking outcome, and did not adjust for any covariates, but did include a random effect of facilitator. The final version of the statistical analysis plan, together with details of any changes to the first version [During the trial, an anomaly in the allocation ratio produced by the online randomisation system became apparent: over the first 69 randomisations the observed allocation ratio was ∼1.25:1 as expected, but over the next 70 randomisations the ratio was ∼5:1. This triggered a corrective action and prevention plan overseen by the sponsor and with the approval of the data monitoring and ethics committee, that included migrating the randomisation system to a new platform. For the remaining randomisations an allocation ratio of 1:1 was specified to return the overall allocation ratio to a figure closer to 1.25:1 and the observed allocation ratio over this third randomisation period was close to 1:1. Full details including baseline characteristics of participants recruited across these time periods and additional sensitivity analyses are provided in the We also conducted a health economic evaluation adopting a “cost-utility” framework, with the incremental resource impact of TANDEM over usual care quantified from an NHS/Personal Social Services perspective and patient outcomes quantified as incremental quality-adjusted life years (QALYs) gained (full details in The impact of the COVID-19 pandemic on the study is described in 