Statistical analyses
REGRESSION
A full statistical analysis plan was designed before the trial (Fehribach et al., Baseline characteristics were tabulated for each condition. Differences across the two groups were assessed with chi-square tests or analyses of variance, as appropriate. To assess whether dropout cases (participants who discontinued treatment after commencing) and complete cases (participants who completed post-test) were non-random, we constructed a balancing table comparing the background characteristics, pre-scores, and other covariates of participants with and without missing outcome observations. Missing outcome values for the dropout sample were imputed using a multiple imputation (MI) procedure that exploited pre-scores and a set of pre-specified background characteristics (gender, age, severity of symptoms, and a dummy variable for whether a participant had missing values on one or more of the background characteristics). Ordinary least squares regression models with pre-scores and background characteristics included were used on an intention-to-treat (ITT) basis to estimate the treatment effect. In addition to standardized mean differences (Cohen's 