Statistical analysis
autoimmunity, DKA
REGRESSION, SECONDARY, AUTOIMMUNITY, RECRUITMENT
By assuming changes in the mean value of fasting C-peptide from 24-month to baseline in the conventional therapy group to be -150 pmol/L (SD = 85), and in the treatment group to be -108 pmol/L (SD = 53), and on the basis of randomization allocated in a 1:1:1 ratio, and a two-sided t test with the type I error rate of 0.025 in either treatment groups compared with conventional therapy (overall alpha of 0.05 for both tests), a sample size of 95 participants would provide 90% power to detect a statistically significant treatment difference, considering a drop-out rate of 20%. Thus, 100 participants for each group were randomly assigned.The endpoint analyses were based on the prespecified FAS population following intention-to-treat principle, defined as those who received at least one dose of treatment. We computed 2-h C-peptide AUC based on the trapezoidal method with the timepoints (0-, 60- and 120-min) measurements of C-peptide during the MMTT, and we conducted a logarithmic transformation on the fasting C-peptide (logFCP) and C-peptide AUC data (log[AUC + 1]) to obtain the normal distribution. ΔC-peptide was computed by C-peptide at 120 min during MMTT minus fasting C-peptide. The primary endpoints compared the absolute difference from 24 months to baseline in the fasting C-peptide between two treatment groups and the conventional therapy group were based on a the mixed model for repeated measures adjusting for age at recruitment, sex, time, treatment-by-time interaction, baseline C-peptide (log[AUC + 1]), baseline C-peptide-by-time interaction, baseline vitamin D concentration, insulin or metformin dosage and DKA at diagnosis. In secondary endpoint analysis, the rate of mean change of C-peptide AUC from baseline to 6, 12, 18 and 24 months was estimated by the mixed model for repeated measures, which included the fixed, categorical effects of group, time, treatment-by-time interaction, sex and the presence of (DKA at diagnosis, as well as the fixed, continuous covariates of baseline C-peptide (log[AUC + 1]), baseline C-peptide-by-time interaction, age at recruitment and baseline serum 25(OH)D concentration. An unstructured covariance was used to model the within-patient errors. Based on the relationship between levels of GADA and the severity of islet autoimmunity and endogenous β-cell function, prespecified treatment effect analyses using mixed effects models were further stratified by GADA levels at baseline (predefined cut-offs: ≤180, 180-≤577 [60In the prespecified subgroup (exploratory) analyses, we used logistic regression models to examine odds ratios (ORs) and their 95% confidence intervals (CIs), for the association between the changes from baseline through 24 months in the C-peptide AUC (two categories: low versus high, median cut-off point) and treatment group, adjusted for age at recruitment, sex, baseline C-peptide (log[AUC + 1]), baseline vitamin D concentration and DKA at diagnosis. Stratification analyses were performed based on age at recruitment, sex, baseline values of BM, DKA, serum 25(OH)D level, HbA1C level, insulin treatment, fasting C-peptide, as well as HLA subtype, number of haplotypes of HLA-DR3, -DR4 or -DR9, and vitamin D receptor (VDR) gene All analyses were conducted using SAS software (version 9.4, SAS Institute Inc.) and R statistical software (version 3.5.2, Vienna, Austria). All reported P values are two-sided, with the threshold of statistical significance of 0.05, and all reported confidence intervals are 95% confidence intervals. Missing data were assumed to be missing randomly.