Table 3.

Data Analysis Methods that Interrogate the Relationship Between Two Sets of Variables

Technique	References	Properties	Description
Biclustering; QR-mode factor analysis	[88,89]	Description	Find groups of individuals that share features on subsets of variables
		Advantages	Combines clustering and factor analysis
		Disadvantages	Hard to assess the cluster significance probabilistically
Standard Multivariate Regression	[90,91]	Description	Assess the linear relationships between two sets of variables
		Advantages	Long history and experience; ease of interpretation and significance
		Disadvantages	Does not work with highly correlated, numerous independent variables
Canonical Correlation; PLS	[20,41,92]	Description	Determine relationships between combinations of variables in two sets
		Advantages	Find patterns in relationships between two large data sources
		Disadvantages	Does not necessarily allow intuitive interpretations
Distance Analysis	[85,93]	Description	Predict the distance between individuals based on one set of dependent variables from multiple independent variables
		Advantages	Can accommodate a very large number of dependent variables
		Disadvantages	Does not find optimal subset of ‘important’ dependent variables
Mixture Models	[94,95]	Description	Identify subgroups of individuals leveraging multiple variables
		Advantages	Parametric formulations have easily interpretable parameters
		Disadvantages	Cannot accommodate a large number of independent of co- variables