Statistical analysis
obesity, Eating Disorder, weight loss
OBESITY, REMISSION, BROWN
Sample size for this maintenance trial was calculated for the comparison between naltrexone/bupropion and placebo, based on estimated rates of response to acute treatments, considering clinically meaningful effects sizes, and performing sensitivity analyses for different outcomes. Power analyses could not be based directly on data from RCTs testing acute effects of naltrexone/bupropion for obesity (Greenway et al., Analyses to compare treatments were all intention-to-treat and were performed for all randomized patients who attended the first treatment session. The two co-primary outcome variables were binge eating and weight loss. Binge eating was analyzed in two complementary ways – i.e. as a continuous variable (monthly frequency of episodes) and as a categorical variable. The categorical outcome variable was binge-eating remission, defined as zero episodes during the previous 28 days on the Eating Disorder Examination interview at posttreatment; any missing data were imputed as failure (i.e. non-remission). Weight (measured) was analyzed as a continuous outcome (percent weight-loss from the beginning of the maintenance trial) and as a categorical outcome. The categorical outcome was clinically-significantly weight loss, defined as 5% or greater weight loss (Brown, Buscemi, Milsom, Malcolm, & O'Neil, For analyses of continuous variables, intention-to-treat analyses used all available data in mixed models without imputation. Variables not conforming to normality were log-transformed prior to analysis. Mixed effects models were fitted with fixed factors including medication during this maintenance trial (naltrexone/bupropion For categorical variable outcomes, intention-to-treat analyses involved generalized estimating equations (GEE) models. Logit link function was used with binomial response distribution. Predictor variables were the same as in the mixed models above. If models encountered convergence problems with all possible interactions, interactions were dropped, and the model refitted until a model with no convergence issues was identified.We explored whether time of measurement (before or during the COVID pandemic) affected the results by including an indicator for timing of measurement as a covariate. The results did not change substantively and therefore the final models are not adjusted for COVID.