Abstract
BackgroundFor 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.ObjectiveWe aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding.MethodsFor non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping.ResultsIf adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression.ConclusionNon-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets.
Highlights
The understanding of immunological mechanisms underlying human disease has largely increased over the last decades
When a complex cascade of regulatory immune mechanisms becomes initiated in a healthy or diseased individual, this leads to gene transcription, which can be measured as gene expression on messenger ribonucleic acid (mRNA) level
Statistical analysis for censored cytokine data from LUMINEX and RT-PCR technology a) Summary statistics are computed by standard methods for left censored data and by the Kaplan-Meier method for right censored data
Summary
The understanding of immunological mechanisms underlying human disease has largely increased over the last decades. In this context, different regulatory mechanisms involving gene expression at messenger ribonucleic acid (mRNA) and protein level (of e.g. cytokines) play an important role. 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. Complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects
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