Statistical analysis
hypoglycaemia
REGRESSION, EVENT, HYPOGLYCAEMIA
As this was an exploratory study, the sample size was selected for feasibility and, thus, was not powered to demonstrate a statistically significant difference in the primary outcome. Baseline characteristics are summarised using means (with SD) or proportions. Assessments of period-level continuous outcomes (e.g. time in range, insulin dose, carbohydrate intake etc.) are presented as means (SD) and mean difference, whereas event-based (e.g. cases with successful hypoglycaemia treatment, time from treatment to euglycaemia etc.) outcomes are presented as either rate ratio (time-to-event outcomes) or OR (dichotomous outcomes). All outcomes are presented as adjusted results together with their 95% CI. For continuous endpoints, the treatment effect was evaluated by comparing treatment groups using a linear mixed model that included the factors treatment (two levels), sequence (two levels), period (two levels) and a random participant effect. Where applicable, outcomes were logarithmically transformed to achieve a normal distribution. In cases where the outcome distribution remained skewed, a Wilcoxon signed-rank test was used. Event-based outcomes were evaluated using either a proportional hazards regression model with γ-distributed random effect (time-to-event outcomes) or a logistic regression model with random participant effect (dichotomous outcomes), both models using sequence, period and event baseline SG value as a covariate. The PRO was analysed using the exact binomial test. Statistical analyses were performed in SAS 9.4 (SAS Institute, Cary, NC, USA). A two-sided 