ArticleBMC medical research methodology2014
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.
Article in BMC medical research methodology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Work Task Association with Lead Urine and Blood Concentrations in Informal Electronic Waste Recyclers in Thailand and Chile.International journal of environmental research and public health · 2021Article
- Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.NPJ digital medicine · 2021Article
- Identifying the predictors of Covid-19 infection outcomes and development of prediction models.Journal of infection and public health · 2021Article
- Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development.International journal of environmental research and public health · 2021Article
- Identifying correlates of Guinea worm (Dracunculus medinensis) infection in domestic dog populations.PLoS neglected tropical diseases · 2020Article
- Comparison of predictive models for hepatitis C co-infection among HIV patients in Cambodia.BMC infectious diseases · 2020Article
- Influenza activity prediction using meteorological factors in a warm temperate to subtropical transitional zone, Eastern China.Epidemiology and infection · 2019Article
- A Data Mining Approach Identified Salivary Biomarkers That Discriminate between Two Obesity Measures.Journal of obesity · 2019Article
- Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology.BMC bioinformatics · 2018Article
- Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases.BioData mining · 2018Article
- Clinical-pharmacogenetic predictive models for MTX discontinuation due to adverse events in rheumatoid arthritis.The pharmacogenomics journal · 2017Article
- Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers.Biomarker research · 2017Article
- Review
- What variables are important in predicting bovine viral diarrhea virus? A random forest approach.Veterinary research · 2015Article
- Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data.Bioinformatics and biology insights · 2015Article
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2 authors.
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Abstract
backgroundBig 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.
methodsWe searched for associations between 303 covariates and influenza infection in 498 subjects (14% infected) sampled from a dedicated cohort. Independent associations were detected using two data mining methods, the Random Forests (RF) and the Boosted Regression Trees (BRT); the conventional logistic regression framework (Univariate Followed by Multivariate Logistic Regression - UFMLR) and the Least Absolute Shrinkage and Selection Operator (LASSO) with penalty in multivariate logistic regression to achieve a sparse selection of covariates. We developed permutations tests to assess the statistical significance of associations. We simulated 500 similar sized datasets to estimate the True (TPR) and False (FPR) Positive Rates associated with these methods.
resultsBetween 3 and 24 covariates (1%-8%) were identified as associated with influenza infection depending on the method. The pre-seasonal haemagglutination inhibition antibody titer was the unique covariate selected with all methods while 266 (87%) covariates were not selected by any method. At 5% nominal significance level, the TPR were 85% with RF, 80% with BRT, 26% to 49% with UFMLR, 71% to 78% with LASSO. Conversely, the FPR were 4% with RF and BRT, 9% to 2% with UFMLR, and 9% to 4% with LASSO.
conclusionsData mining methods and LASSO should be considered as valuable methods to detect independent associations in large epidemiologic datasets.
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