Header

UZH-Logo

Maintenance Infos

Improving epidemiologic data analyses through multivariate regression modelling


Lewis, Fraser; Ward, Michael (2013). Improving epidemiologic data analyses through multivariate regression modelling. Emerging Themes in Epidemiology, 10:4.

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.

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.

Statistics

Citations

Altmetrics

Downloads

269 downloads since deposited on 30 May 2013
26 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:05 Vetsuisse Faculty > Chair in Veterinary Epidemiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2013
Deposited On:30 May 2013 09:25
Last Modified:06 Aug 2017 17:36
Publisher:BioMed Central
ISSN:1742-7622
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/1742-7622-10-4
PubMed ID:23683753

Download

Preview Icon on Download
Preview
Content: Accepted Version
Filetype: PDF
Size: 285kB
View at publisher
Preview Icon on Download
Preview
Content: Published Version
Filetype: PDF
Size: 388kB
Licence: Creative Commons: Attribution 2.0 Generic (CC BY 2.0)