4.5. Statistical Analyses
REGRESSION
The summary statistics are presented as means ± SD for continuous variables and counts and percentages for discrete variables. Our primary objective was to examine if the targeted metabolites were different after each of the strawberry vs. control phases as well as the baseline. To assess differences, we used a mixed-model ANOVA to examine the main effects of the treatment, time, and interaction to examine the differences in outcomes at the end of each four-week phase of intervention. Baseline values were included as covariates for each outcome variable. The outcomes were modeled as repeated measures, with the subject as a random effect and with unstructured variance for treatment/time. The sequence of intervention was included in all models to test for carry-over effects, and none were detected. We also examined differences in habitual dietary nutrients and flavonoids using the same mixed-model approach. To examine untargeted metabolites, the LASSO logistic regression model was used to identify significant metabolites that were predictive of the strawberry treatment phases compared with the baseline. The LASSO model was trained on 80% of the total dataset and then tested on the remaining 20%. A receiver operating characteristic curve analysis was performed on the metabolites related to strawberry intervention individually in the test set data. The data were auto-scaled, and the odds ratios are presented for each significant metabolite for comparisons between the baseline and strawberry-treatment phases. The power for this study was calculated based on previous differences in serum LDL-cholesterol to achieve 80% power at 0.05 α level [