Statistical Analysis
DERS-16, NSSI, self-destructive behaviors, assessor-rated NSSI
REGRESSION
See eMethods in Treatment effects were evaluated according to the intention-to-treat principle. The primary outcome analysis of self-reported NSSI included treatment condition and weekly self-reports of NSSI frequency (using the DSHI-Y). A zero-inflated negative binomial generalized linear-mixed effects regression model was used to estimate the trend over time for self-reported NSSI counts measured every week by including an interaction term between treatment condition and time while allowing for a random intercept and slope per individual. Treatment condition and study site were included as categorical variables and time (weeks zero to 16) was treated as a continuous variable. Exploratory analyses investigated whether certain relevant TAU or client characteristics moderated treatment effects (eMethods in For pretreatment and 1-month and 3-month posttreatment analyses, each assessment point was included as a dummy variable in separate mixed effects regression models including a random intercept, and an interaction term between treatment condition and the categorized time variable (pretreatment, 1-month posttreatment, 3-month posttreatment). Treatment condition and study site were included as categorical variables. For assessor-rated NSSI frequency (DSHI-Y), a zero-inflated negative binomial regression model was fitted to the NSSI counts. For counts on other self-destructive behaviors (BSL-Supplement), zero-inflated Poisson regression models were used. Model comparisons are available in eTable 10 in To determine whether change in week-to-week emotion dysregulation (DERS-16) during treatment mediated the overall effect of IERITA on week-to-week change in the self-reported NSSI frequency, we employed a parallel process latent growth curve modeling approach for mediation.