# Table 2 Selection of important statistical methods suitable for the analysis of immunological data.

Example of research question Type of data [D: dependent, I: independent] Other data assumptions Statistical method1
Univariate techniques
Univariate group mean comparison techniques
Compare expression of a cytokine between two independent groups (e.g. treatment vs. control) D: continuous
I: categorical
Normal distribution homogeneity of variances t-test
D: continuous or ordinal
I: categorical
Mann Whitney-U test
Compare expression of a cytokine between two related groups (e.g. before and after treatment) D: continuous
I: categorical
Normal distribution, homogeneity of variances Paired t-test
D: continuous or ordinal
I: categorical
Wilcoxon rank sum test
Compare expression of a cytokine between three or more independent groups defined by one factor (e.g. treatments A, B, C) D: continuous
I: categorical
Normal distribution, homogeneity of variances One-way analysis of variance
D: continuous or ordinal
I: categorical
Kruskal Wallis – H test
Compare expression of a cytokine between three or more related groups (e.g. measurements 1, 2, and 3 weeks after treatment) D: continuous
I: categorical
Multivariate normal distribution, assumptions about covariance Repeated measurements analysis of variance
D: continuous or ordinal
I: categorical
Friedman's ANOVA
Correlation and regression analysis
Quantify association between two cytokines or a cytokine and another continuous variable D: continuous
I: continuous
Linear relationship, normality Pearson correlation coefficient
D: continuous or ordinal
I: continuous or ordinal
Linear relationship Spearman rank correlation coefficient
Predicting expression of a cytokine by a continuous independent variable D: continuous
I: continuous
Specified relationship (e.g. linearity for linear regression), normal distribution (for parametric regression) Univariate regression
Multivariate techniques
Multivariate correlation and regression techniques
Quantify associations between two cytokines adjusted for the effect of a third continuous variable All variables: continuous Linear relationship, normality Partial correlation coefficient
Predicting a continuous outcome (e.g. a cytokine) by several continuous or categorical independent variables D: continuous
I: continuous, ordinal or categorical
Specified relationship (e.g. linearity for linear regression), normal distribution for parametric regression, No multi-collinearity Multiple regression
Specified relationship, multi-collinearity Partial least squares regression
Quantifying the magnitude of correlation between two groups of continuous variables (e.g. Th1 and Th2 related cytokines) All variables: continuous   Canonical correlation analysis
Multivariate group mean comparison procedures
Compare cytokine expressions between three or more independent groups defined by two or more factors (e.g. treatment and gender) D: continuous
I: categorical
Normal distribution, homogeneity of variances Multi-way analysis of variance (ANOVA)
Simultaneously compare expressions of two or more cytokines between three or more independent groups defined by two or more factors D: continuous
I: categorical
Multivariate normal distribution, homogeneity of covariance matrices Multivariate analysis of variance (MANOVA)
Compare cytokine expressions between three or more related groups defined by two or more factors (e.g. measurements at different time points during a study and treatment) D: continuous
I: categorical
Multivariate normality, homogeneity of covariance matrices Multi-way repeated measurements analysis of variance
Grouping set of correlated cytokines to summary variables ("principal components") All variables: continuous High degree of multicollinearity Factor analysis/Principal components analysis
Grouping subjects in homogenous subgroups according to similar expression levels of two or more cytokines All variables: continuous Low degree of multicollinearity Cluster analysis
Classification procedures
Explaining or predicting group membership of two or more independent groups by cytokine levels D: categorical
I: continuous
Multivariate normal distribution, equal covariance matrices, low degree of multicollinearity Linear discriminant analysis
Explaining or predicting group membership of two independent groups by cytokine levels D: categorical
I: continuous, ordinal or categorical
Logistic regression
Explaining or predicting group membership of three or more groups by cytokine levels D: categorical
I: continuous, ordinal or categorical
Multinomial logistic regression