Statistical analysis
DISEASE PROGRESSION
The study sample size was determined as adequate to provide a reliable safety assessment of the tested doses and to support the preliminary assessment of clinical response endpoints. The sample size used in this study was aligned with a similar study of peptide immunotherapy which found the safety profile to be as expected [All clinical response analyses were performed by dose and overall. For MMTT analysis, changes in the area under the curve (AUC) C-peptide from baseline at each visit were analyzed using a one-way analysis of covariance (ANCOVA) method. Fasting C-peptide was normalized by the glucose level and changes in fasting C-peptide and change in HbA1c were analyzed using one-way ANCOVA (F test). Change in insulin dose was analyzed using a Disease progression was measured as a primary endpoint. For this analysis, a linear mixed effect model (random effect – inter-patient variability; fixed effects – all available covariates) was used to model the progression of endpoint fasting C-peptide over time. Covariates were chosen from a list of candidates (baseline laboratory values, autoantibodies levels, HLA status, and patient characteristics); as there were multiple candidates, there were multiple possible combinations of covariates. Only covariates with strong associations with C-peptide/glucose at least at two visits were included. The combination of covariates was selected via a data-driven approach: they were retained if significantly associated with C-peptide/glucose change from baseline at Week 24 (Autoantibody levels were analyzed separately for each autoantibody using an ANCOVA model with ratio to baseline as dependent variable, baseline value as covariate, and treatment as factor. The ratios between each treatment group and placebo were analyzed using confidence interval and 