Data analysis
REGRESSION, SECONDARY, RECRUITMENT
Descriptive statistical analyses were conducted for the feasibility and potential primary and secondary outcomes at baseline and each follow-up time point. Although this feasibility study was not powered to detect differences between groups in questionnaire-based outcomes, multi-level linear regression models were fitted to the data from each questionnaire to explore the direction and magnitude of effects. This method of analysis uses data from all time-points, and provides unbiased estimates, providing data can be assumed to be missing-at-random.A cost-consequence analysis (CCA) was performed from a National Health Service (NHS) perspective. This analysis is commonly used in the literature to investigate the intervention’s impact on costs and health outcomes in a feasibility study [Interview data were analysed using a directed content analysis technique [Data relating to participation in the trial and SCG and in the ACT intervention were coded separately. Codes were agreed between the coding team (MB, HG, SB, EB) using one transcript before MB and EB coded all transcripts independently. Similarities and differences in views between the groups were highlighted and examples of disconfirming data were sought for each acceptability construct. The coding frequencies were presented as counts along with supporting quotes for each TFA component. Coding was discussed and agreed at multidisciplinary team meetings including the service user advisors.This was a change from protocol where inductive thematic analysis was planned. However, given findings of successful recruitment but high attrition, it was felt that using a focused, structured approach to understand the acceptability, of both the trial processes and experience of the ACT group, would enable identification of specific areas to be addressed in future work. The TFA has been applied in this way to various interventions, including a men’s mental health promotion program [