Statistical Methods
REGRESSION
A sample size of approximately 480 patients was considered appropriate to demonstrate the superiority of aboBoNT-A compared with placebo, the non-inferiority of aboBoNT-A compared to active comparator, and the assessment of aboBoNT-A safety according to the China Food and Drug Administration guidelines. Statistical assumptions were based on previously published literature [Efficacy analyses were performed on the modified intent-to-treat population (all randomized patients who received study treatment in  ≥ 1 injection site and had both cycle 1, day 1 [baseline] and cycle 1, day 29 assessments) and on the open-label population (all randomized patients who received any dose of open-label aboBoNT-A; cycles 2–5). The superiority of aboBoNT-A to matching placebo was tested using a multivariate logistic regression model including treatment group, stratification factors of gender and baseline severity score (glabellar lines at maximum frown measured by the ILA), treatment center as an explanatory variable, and responder (yes or no) as a response variable. A two-step hierarchical testing procedure was applied to control the family-wise type I error rate (using 0.025 significance levels). If superiority was demonstrated, non-inferiority was tested one-sided at the significance level of 0.025. If the lower limit of the 95% confidence intervals (CIs) of the treatment difference was  ≥ −15%, aboBoNT-A was considered to be non-inferior compared to the active comparator.The multivariate logistic regression model was used for analysis of the proportion of responders based on photographs of patient glabellar lines at maximum frown and proportion of responders with respect to SGA score. A linear mixed model (which included stratification factors of gender and baseline severity score, and center as fixed effect) was used to analyze mean SGA scores at cycle 1, day 29; the test was two-sided at the significance level of 0.05. Time to onset of treatment response was analyzed using the Cox model, and mean change from baseline in patient’s self-perception of age was analyzed by mixed model with repeated measures. The agreement between different measures was determined using Spearman’s rank correlation coefficient, weighted kappa statistics, and 95% CIs. Safety endpoints were presented using descriptive statistics.