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Emerg Themes Epidemiol. 2013 May 17;10(1):4. doi: 10.1186/1742-7622-10-4.

Improving epidemiologic data analyses through multivariate regression modelling.

Author information

1
Section of Epidemiology, VetSuisse Faculty, University of Zürich, Winterthurerstrasse 270, Zürich, CH 8057, Switzerland. fraseriain.lewis@uzh.ch.

Abstract

: Regression modelling is one of the most widely utilized approaches in epidemiological analyses. It provides a method of identifying statistical associations, from which potential causal associations relevant to disease control may then be investigated. Multivariable regression - a single dependent variable (outcome, usually disease) with multiple independent variables (predictors) - has long been the standard model. Generalizing multivariable regression to multivariate regression - all variables potentially statistically dependent - offers a far richer modelling framework. Through a series of simple illustrative examples we compare and contrast these approaches. The technical methodology used to implement multivariate regression is well established - Bayesian network structure discovery - and while a relative newcomer to the epidemiological literature has a long history in computing science. Applications of multivariate analysis in epidemiological studies can provide a greater understanding of disease processes at the population level, leading to the design of better disease control and prevention programs.

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