ArticleEnvironmental health : a global access science source2014
The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees.
Article in Environmental health : a global access science source, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood.The Science of the total environment · 2024Article
- Prenatal Lead Exposure Is Associated with Reduced Abundance of Beneficial Gut Microbial Cliques in Late Childhood: An Investigation Using Microbial Co-Occurrence Analysis (MiCA).Environmental science & technology · 2023Article
- Nutritional Modulation of Associations between Prenatal Exposure to Persistent Organic Pollutants and Childhood Obesity: A Prospective Cohort Study.Environmental health perspectives · 2023Article
- 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
- Identifying environmental exposure profiles associated with timing of menarche: A two-step machine learning approach to examine multiple environmental exposures.Environmental research · 2021Article
- Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks.Frontiers in plant science · 2021Article
- Joint and interactive effects between health comorbidities and environmental exposures in predicting amyotrophic lateral sclerosis.International journal of hygiene and environmental health · 2021Article
- IDENTIFYING MAIN EFFECTS AND INTERACTIONS AMONG EXPOSURES USING GAUSSIAN PROCESSES.The annals of applied statistics · 2020Article
- Chemical mixtures and neurobehavior: a review of epidemiologic findings and future directions.Reviews on environmental health · 2020Review
- Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps.Current environmental health reports · 2020Review
- An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat.Machine learning · 2020Article
- Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.BMC medical research methodology · 2019Article
- Statistical Methodology in Studies of Prenatal Exposure to Mixtures of Endocrine-Disrupting Chemicals: A Review of Existing Approaches and New Alternatives.Environmental health perspectives · 2019Review
- rFSA: An R Package for Finding Best Subsets and Interactions.The R journal · 2018Article
- Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression.Environmental health : a global access science source · 2018Article
- Review
- Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science.Current environmental health reports · 2017Review
- A systematic comparison of statistical methods to detect interactions in exposome-health associations.Environmental health : a global access science source · 2017Article
- Analytic Complexity and Challenges in Identifying Mixtures of Exposures Associated with Phenotypes in the Exposome Era.Current epidemiology reports · 2017Review
- Comparison of small-area deprivation measures as predictors of chronic disease burden in a low-income population.International journal for equity in health · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a need to evaluate complex interaction effects on human health, such as those induced by mixtures of environmental contaminants. The usual approach is to formulate an additive statistical model and check for departures using product terms between the variables of interest. In this paper, we present an approach to search for interaction effects among several variables using boosted regression trees.
methodsWe simulate a continuous outcome from real data on 27 environmental contaminants, some of which are correlated, and test the method's ability to uncover the simulated interactions. The simulated outcome contains one four-way interaction, one non-linear effect and one interaction between a continuous variable and a binary variable. Four scenarios reflecting different strengths of association are simulated. We illustrate the method using real data.
resultsThe method succeeded in identifying the true interactions in all scenarios except where the association was weakest. Some spurious interactions were also found, however. The method was also capable to identify interactions in the real data set.
conclusionsWe conclude that boosted regression trees can be used to uncover complex interaction effects in epidemiological studies.
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