4.4. Statistical Analyses
The distribution of the data was analysed, and non-parametric analysis or parametric analysis was performed on log-transformed data.All relevant study data were tabulated with descriptive statistics, including median and interquartile range (IQR) for the continuous variables, and frequencies and proportions for the categorical variables. Group-wise comparisons were performed using chi-square or Wilcoxon/Mann–Whitney tests. Associations between the biomarkers and clinical variables were assessed using Spearman rank correlation.Longitudinal linear models (using generalized least squares to account for correlated measurements within patient) were fitted for the analysis of the biomarker data. The analysis was done using log(biomarker) as the dependent variable. Treatment, site, age category (≤55 or >55 years), visit, and treatment-by-visit interaction were added as factors in the model as randomization stratification factors, together with the baseline log(biomarker) value as a covariate to improve the efficiency of the treatment effect estimate. An unstructured covariance matrix was used. Heterogeneity of the treatment effect with respect to sex, age category, or cAKUSSI at baseline was investigated by adding the respective factors and appropriate interaction terms to the base model. Estimates on the log-scale were back-transformed to the original scale to represent geometric means. Contrasts between treatment groups were back-transformed to represent ratios of geometric means.All statistical analyses were done with R version 4.1.3 (R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL 