Improving epidemiologic data analyses through multivariate regression modelling.
- 2013-05-21
Published in:
- Emerging themes in epidemiology. - 2013
English
: 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.
- Language
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- English
- Open access status
- gold
- Identifiers
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- DOI 10.1186/1742-7622-10-4
- PMID 23683753
- Persistent URL
- https://folia.unifr.ch/global/documents/261101
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