For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects.I aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, I assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confoundin
