Statistical Analysis
REGRESSION
Analysis of the eyelid closure data from the crossover design was performed using R software, version 4.0.2. The analysis was applied to the parameters derived from video analysis: number of eyelid closures per 100 seconds, median eyelid closure duration, median eyelid closure amplitude, and median max eyelid closure velocity. Linear mixed-effects regression (LMER) models were fitted utilizing the R lme4 package. The residuals met conventional criteria for the regression model assumptions. Additionally, the homogeneity of variance assumption was evaluated using Leven's test and the assumption of normality was assessed with Shapiro-Wilk normality tests. Multiple comparison tests to find significant differences between the treatment group means were performed using the Bonferroni-Holm method and by-passing model estimates through ANOVA. Carryover effects were evaluated by assessing the significance of a treatment-session interaction term and by comparing those who received CBD in session 2 with those who received CBD in session 4 by To avoid fitting an overly complex model, variables were selected a priori and evaluated for significance. Each variable was evaluated to see how its inclusion affected the model's Akaike information criterion (AIC) score, coefficient of determination (RTo further assess the clinical implications of CBD in patients with BPS and to validate the data generated from our eye tracking software, we performed a subanalysis on the data from the JRS. The JRS data were analyzed by means of ANOVA, LMER, and multiple comparisons of means using Tukey's contrasts. Visual representation of the JRS data and SEM can be found in 