Biomarkers for pancreatic cancer: Recent achievements in proteomics and genomics through classical and multivariate statistical methods

Table 1 Statistical methods adopted in the identification of biomarkers for pancreatic cancer

Type of statistical method	Method adopted
Classical mono- and multi-variate methods	Student t-test (parametric)
	Mann-Whitney U-test (non-parametric)
	T2 Hotelling
	ANOVA and MANOVA
	Bayes factors
Unsupervised pattern recognition methods	Principal Component Analysis
	Cluster Analysis
	Multidimensional Scaling
Supervised classification methods	SIMCA
	Ranking-PCA
	O-PLS
	CART
	Random Forests
Methods for determining survival outcomes	Kaplan Meyer functions
	Cox Regression
Other methods	PAM
	Metropolis algorithm and Monte Carlo simulation

PCA: Principal component analysis; SIMCA: Soft independent model of class analogy; PLS: Partial least squares; CART: Classification and regression tree.