Statistical methods
REGRESSION, SECONDARY
Statistical analyses were conducted using SAS v. 9.4. Descriptive statistics were used to compare intervention and control groups at baseline. An intention-to-treat approach was used in the analyses of all trial outcomes, and all students with baseline data information at all participating schools were included in the analyses. Students who reported smoking above 65 cigarettes per day at baseline and follow-up and concurrently answered the highest response options to other questions (e.g., reported believing that all students at their schools smoked cigarettes, smoked the maximum number of cigarettes during school days, i.e., 30 cigarettes, or gave unserious answers in open-text questions), thus, indicating sabotage of survey responses, were excluded for further analyses (n = 4).Missing data were handled with multiple imputation in two steps. First, the missing values in the baseline data were imputed. Next, missing follow-up data were imputed based on baseline data and non-missing values for primary and secondary outcomes at follow-up. Using the PROC MI procedure in SAS, two times of 20 rounds of imputations were carried out. In the first round, the school class variable was not included in the imputation model, while in the second round, the school class variable was included in the imputation model to account for dependency within school classes. Using SAS PROC MIANALYZE, the imputed datasets were summarized to estimate intervention effects. Complete case analysis was utilized as a sensitivity analysis; the results did not differ substantially and therefore, only the intention-to-treat results are presented.The effect analyses were based on between-group comparisons based on the five-month test and adjusted by the baseline score of the outcome variable if measured, and baseline age, gender, and SEP as covariates to increase the precision of effect estimates. The stratification variable, school type, was also included as a covariate. Each model included class as a random effect to account for the cluster effect. Class-level intraclass correlations (ICC) are presented for the smoking outcomes. Multilevel general linear regression modeling estimated the association between the intervention and daily cigarette consumption among daily smokers. We graphically checked model assumptions of normal distribution and variance homogeneity of the residuals that indicated influential outliers that violated the assumptions. We examined these outliers, and the values were all above 25 cigarettes per day (range: 28–48 cigarettes). Therefore, we performed a sensitivity analysis in which we excluded students with values above 25 (n = 8), resulting in a reasonable model fit (see results in Additional file 2). The dichotomous outcomes, i.e., smoking status and secondary outcomes, were assessed with multilevel logistic regression models.We examined preplanned subgroups within intervention schools (baseline smoking status, age, gender, and school type) and possible moderating effects on the daily cigarette consumption and smoking status at follow-up. Baseline smoking status was categorized into daily smoking, regular smoking, and non-smoking. Age was divided into < 18 years and ≥ 18 years. School type was categorized as (1) VET schools: social and healthcare, (2) VET schools: technical and commercial, and (3) PBE schools. Moreover, we conducted a per-protocol analysis to investigate the effect of the actually delivered intervention.