title: Causal diagrams and multivariate analysis I: a quiver full of arrows.
abstract: How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways.
authors.affiliation: Assistant Professor, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX. Electronic address: dajupite@utmb.edu.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Jupiter
mesh_terms: D015984:Causality; D002986:Clinical Trials as Topic; D006801:Humans; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D012307:Risk Factors
keywords: confounder; effect modification; multivariate analysis; precision variable