Statistical Analysis
Patient demographic data and baseline clinical history were summarized using median (IQR) for continuous variables and frequency (percentage) for categorical variables. Continuous variables were compared using the Wilcoxon rank-sum test, whereas categorical variables were compared using the Pearson’s chi-squared test. We also summarized the number (percentage) of patients who remained in the study at three months.
Glucose levels were analyzed as a dichotomized variable (IFG) if levels were greater than 100 mg/dL. For the continuous glucose outcome, we used the linear mixed effects model with fixed time and group (baseline IFG status Yes/No) effect and random subject effect to model the trajectory of glucose levels over time. Specifically, time was modeled as piecewise linear with change points at 1 and 3 months. The random effects model takes into account the correlation between the observations within the same subject. Model estimates of glucose levels and their 95% confidence intervals were summarized for both the IFG and the normal fasting glucose group at 1 and 3 months. In addition, we tested for the difference in glucose levels for 0 versus 3 months and reported the p-values.
For the binary impaired fasting glucose (Yes/No) outcome, we used the generalized linear mixed effects model with the logit link function to model the proportion of IFG patients over time. The covariates structure was the same as above. The estimated proportion of IFG patients and their 95% confidence intervals were reported. Like before, we also tested for the difference in proportions for 0 versus 3 months and reported the p-values. A p-value < 0.05 was considered statistically significant. R version 3.2.3 (www.r-project.org) was used for the analyses.