Detection of independent associations in a large epidemiologic dataset: a comparison of random forests, boosted regression trees, conventional and penalized logistic regression for identifying independent factors associated with H1N1pdm influenza infections

BACKGROUND: Big data is steadily growing in epidemiology. We explored the performances of methods dedicated to big data analysis for detecting independent associations between exposures and a health outcome. METHODS: We searched for associations between 303 covariates and influenza infection in 498...

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Detalles Bibliográficos
Autores principales: Mansiaux, Yohann, Carrat, Fabrice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4146451/
https://www.ncbi.nlm.nih.gov/pubmed/25154404
http://dx.doi.org/10.1186/1471-2288-14-99

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4146451/
https://www.ncbi.nlm.nih.gov/pubmed/25154404
http://dx.doi.org/10.1186/1471-2288-14-99

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