2.9. Data Analysis
SECONDARY
This is a secondary analysis of data from a study previously reporting no differences in BMI by group [To determine how raw HEI scores differed over time across intervention groups (i.e., experimental and comparison groups), the association of the intervention with the outcome of HEI scores was tested using the generalized estimating equation model (GEE) specifying an exchangeable correlation structure for the repeated time measurements. A log-normal outcome was specified in the model with a log link function. This was accomplished by specifying the outcome distribution during the modeling process and allowed the response variable to be modeled as a random variable with a Poisson distribution. The randomized group, time and interactions were tested, with the latter serving as the parameter for the effect of the randomized group on the change in HEI scores over time. Since the 24-HDR assessments were conducted on a weekend and weekday, day type (weekend and weekday) was included as a fixed effect. A significant randomized group × time interaction warranted pairwise comparisons between the time points with adjustment for pairwise comparisons using Tukey’s method. To determine which variables to include as covariates in the models, general linear modeling with backward selection (GLMSELECT procedure in SAS) was performed to test the association of the HEI scores as outcomes with the demographic variables. The relationships between the raw HEI scores with covariates were also assessed using the correlation coefficient (Spearman’s rho) for continuous variables, and the Wilcoxon or Kruskall–Wallis test for categorical variables. To keep the models consistent for comparison purposes, a covariate was included in all models if it was associated (Given the observed associations of the raw HEI scores with the covariates selected for modeling, adjusted HEI scores were calculated to present more precise estimates (