Data Analytic Strategy
REGRESSION
Analyses were performed on prespecified hypotheses for the primary outcome variable of exposure to SV using an intent-to-treat protocol in which participants were analyzed according to their assigned study conditions [Descriptive statistics were created for all study measures, with mean and SD for continuous variables and frequency distribution for categorical variables. Comparisons were conducted of baseline findings across study characteristics and outcomes to determine whether participants who completed the intervention were similar to those who did not.A substantial number of participants were expected to report not having an experience of exposure to SV, to not have engaged in bystander behavior, and to not having previously consumed alcohol. To account for these zero occurrences, a comparison of the mean occurrence for each outcome across the baseline and 6-month follow-up time points was accomplished with a 2-stage modeling process using zero-inflated regression models. In the first stage, a logistic regression model was used to model occurrence or not for each outcome, and in the second stage, a Poisson or gamma regression model was used to model each outcome for those who had at least 1 occurrence. Exposure to SV and most of the alcohol consumption measurements were count outcomes and modeled with the Poisson distribution. The alcohol protective and dating risk behaviors scales were both continuous and reasonably symmetric. A multilevel model in the form of a general linear mixed model was used. The Bystander Behavior Scale was skewed and assessed with logistic regression; heavy drinking outcomes were continuous and skewed and were modeled with a gamma distribution.Participants were assessed at baseline and at 6-month follow-up. Repeated measurements on each participant results in within-participant correlation. This was accounted for by estimating each zero-inflated model with population-averaged effects using a marginal model and generalized estimating equations. Each zero-inflated marginal model included fixed effects to control for study site, race, ethnicity, place of living, relationship status, sexual orientation, engagement in athletics, job status, ever drank alcohol, time, and study condition. A time × study condition interaction term was included in each model to assess and test for intervention effectiveness. The interaction term quantifies the relative change in the outcome over time across study conditions. Intervention effects were estimated with odds ratios for logistic models, incidence rate ratios (IRRs) for Poisson models, and regression coefficients for gamma models. SAS software (version 9.4; SAS Institute Inc) was used for all statistical analyses.