Statistical analysis
REGRESSION
Descriptive statistics are reported using means and standard deviations for continuous variables and frequencies and percentages for categorical variables. To assess the impact of the HL intervention on participant outcomes, we performed regression modeling with fixed-effect repeated measures to account for the longitudinal data collection at baseline, eight weeks, and 20 weeks. We used logistic regression for dichotomous outcomes and linear models with transformations as needed for continuous outcomes. The candidate variables for inclusion in the model are the demographic, descriptive, and outcome variables listed in Table To prevent over-fitting and the small sample size relative to many predictors, we used a forward selection technique at an alpha level of 0.05. This approach allowed us to identify a parsimonious set of variables independently associated with the outcome variables. We examined residual, quantile, and leverage plots to test the model's fit. Trajectories over time and interactions are reported. The statistical package used was SAS 9.4 (SAS Institute, Inc., Cary, NC). The statistician and data manager, uninvolved in protocol management, conducted data verification and analyses.