Improving epidemiologic data analyses through multivariate regression modelling

• Published in: Emerging themes in epidemiology 2013-05, Vol.10 (1), p.4-4, Article 4
• Main Authors: Lewis, Fraser I, Ward, Michael P
• Format: Article
• Language: English
• Subjects: Analytic; Medical research; Medicine, Experimental; Prevalence studies (Epidemiology); Regression analysis
• ISSN: 1742-7622; 1742-7622
• DOI: 10.1186/1742-7622-10-4

: 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.