Data Analysis
anxiety
REGRESSION
Univariate and bivariate statistics were estimated with IBM SPSS version 26.0 (IBM Corp., Latent growth curve modeling (LGM) was used separately on youth- and parent-rated anxiety to model treatment response in anxiety symptoms over time at group and individual levels. Three slope factors were used to represent factorial differences over time, between T1-T2, T1-T3, and T1-T4, to use all available information for estimation. Only the estimated results from the change in T1-T4 (pre- to long term follow-up) were reported. The time spacing between observations varied across individuals. Using all available repeated data increases the precision of the estimates. The prediction models defined time as months. To make the models more parsimonious the prediction models assumed random intercept (pre-treatment level) and fixed slopes (change) (Newsom, The slopes were regressed on the predictors, both main effects and pre-specified interaction terms, to assess their influence on symptom change. Prediction models of loss of principal and all diagnoses were based on the logistic regression (logit). An odds ratio greater than one predicts higher odds of diagnostic recovery associated with the predictor. Adherence and competence were highly correlated (The predictor variables for alliance were calculated using the total score of both early and late alliance (sum score of both sessions assessed for alliance). Separate scores for “therapist-rated alliance” and “youth-rated alliance” were computed as these were separate predictors. The predictor variable “adherence” was the total adherence score based on CAS-CBT, whereas the predictor variable “competence” was the residualized score for total competence score on CAS-CBT. The predictor variables “type of treatment format” (ICBT/GCBT) and “formal CBT training” (yes/no) were dichotomous variables, whereas the predictor variable “symptom severity” was computed using a sum score of CSR for the three inclusion diagnoses at pre-treatment. For outcome variables, youth-rated and parent-rated anxiety symptoms were treated as separate outcome variables, as the youth treatment literature recommends a multi-informant perspective (Ogles, In the total sample of N = 170, the highest frequency of missing data was observed for SCAS-C (27%) and SCAS-P (30%) at T4. Regarding the analyses of loss of principal and loss of all diagnoses, missing data were present for 27% of the patients at T4. Some missing data were also present in the predictor variables (up to 11%). However, due to the analysis strategy, all cases were used for estimation of these models, N = 124 and N = 170 in the loss of diagnoses and level and change in the SCAS variables, respectively.