Statistical Analyses
REGRESSION, SECONDARY
On the continuous primary outcome sick leave (total number of days from register data) and secondary outcome variables (questionnaires), factorial repeated measure of variances with covariates (ANCOVA) were used, with the assumptions for analyses checked. The main outcomes were time (baseline/post intervention/6-months follow-up) * condition (ECO group/PE group), on each variable. Further, on the sick leave outcome, self-reported sick leave at 6-months follow-up was analyzed using chi-square test, to evaluate difference between conditions. The effect on number of individuals with prevalence of a sick leave spell, based on register data, was analyzed using hierarchical logistic regression, to evaluate impact of condition on sick leave, while controlling for baseline values and co-variates.Results were analyzed on the individual level. In all analyses of outcomes, type of workplace was included as a co-variate, based on potential workplace level effects and the identification of interactions with outcome variables at baseline. Data assessment before or during the Covid-19 pandemic was also included as a co-variate. Intra-cluster correlation coefficients (ICC) were calculated for the outcomes, for the clusters of workplace-units. Based on low to moderate ICC values for the primary outcome, and a relatively small number of clusters, the cluster level of randomization units was not further accounted for in the analyses.Analyses were based on an intention to treat (ITT) approach, including all participants with baseline assessments available. An ITT approach, with imputation of missing data, reduces the risk of biased and incorrect inference in randomized controlled trials [In addition to the ITT approach, analyses were performed on a sample of per-protocol participants (participating in the interventions). Results did not differ, and analyses on the ITT sample are reported together with results from individuals with complete data. Due to differences between conditions on stress-related ill-health at baseline, additional analyses were performed to rule out this potential impact. A matching was performed based on propensity scores. Results from the analyses on the matched sample did not differ, and results from the original sample are reported.All statistics were conducted using SPSS version 27.0, IBM statistical software. For analyses of group differences at baseline, and analyses of non-responders, t-tests or chi-square test were used. Throughout, comparisons were two-tailed and were treated as statistically significant at the level of p < 0.05.