Statistical analyses
psychiatric
An intention-to-treat analysis was conducted in the present study and included all participants randomized in each group whether or not they received the intervention or were lost to follow-up. To detect a reduction of 30% in the rate of compulsory admissions to psychiatric hospitals during the 12-month follow-up, the planned sample was 200 per group, i.e., 400 in total. This number of subjects allowed for a minimum cost difference of €320 with a standard deviation of 1,000, at a statistical power of 90%.Missing data were addressed using multiple imputations (van Buuren, A mixed model for longitudinal utility values was used to control for potential bias due to intra-patient correlated data and the existence of co-variates influencing quality of life. Between-group differences in service use and total costs were estimated using generalized estimating equations (GENLIN function) applying a Poisson distribution with a link log due to skewness. Mean differences and beta coefficients with 95% CI were provided.We used non-parametric bootstrapping (with 5,000 replications) to resample observations. The bootstrap results were combined to calculate the mean values for costs and utilities, and the SEs for the imputed values were used to calculate 95% CIs. The incremental cost per QALY gained was then calculated as mean incremental costs divided by mean incremental QALYs and reported where relevant. Uncertainty in the cost-effectiveness results was analysed using both univariate deterministic and probabilistic sensitivity analyses.Statistical analyses were performed using R Studio (R version 4.0.2, RStudio, Inc., Massachusetts, USA) and TreeAgePro 2019.