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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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| Autores principales: | , |
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| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
BioMed Central
2014
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4146451/ https://ncbi.nlm.nih.gov/pubmed/25154404 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2288-14-99 |
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