Statistical analyses
REGRESSION, SECONDARY
Perceived body shape was not a primary outcome in the larger CALERIE study. Therefore, the analyses in this paper are secondary and should be considered exploratory in nature. Baseline differences in groups were analyzed using independent samples Boxplot of Body Shape Questionnaire score and sex. Graph represents the averaged Body Shape Questionnaire score across baseline, 12 and 24 monthsWe used Pearson’s correlations and regression analyses to understand and model the relationships between the change in BSQ score and each of the predictors. Data were inspected for assumptions of regression analysis, including linearity, normality, and equal variance of residuals. No substantive violations of the assumptions were identified. Proc GLM was used for all linear regression models and included beta estimates, We also included AIC (Akaike information criterion) and BIC (Bayesian information criterion) to compare the relative goodness-of-fit for competing models. AIC and BIC each characterize the degree to which the BSQ scores are explained by the terms in the model, but with slightly different penalties for adding terms to the model. Each of AIC and BIC will identify an optimal model in terms of good but parsimonious fit. For both AIC and BIC, lower scores correspond to better models.Lastly, we conducted an average causal mediation effect (ACME) analysis to determine whether or not body fat percentage is a mediator between treatment group and BSQ score. This was performed by determining the difference between the total effect of treatment on BSQ and the direct effect of treatment group on BSQ score after accounting for body fat percentage (total–direct effect). The estimate (difference from zero), 95% confidence interval, and 