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The identification of complex interactions in epidemiology and toxicology: a simulation study of Boosted Regression Trees
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Occupational and Environmental Medicine.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cardiovascular epidemiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Occupational and Environmental Medicine.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
2014 (English)In: Environmental health, ISSN 1476-069X, Vol. 13, 57- p.Article in journal (Refereed) Published
Abstract [en]

Background: There 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. Methods: We 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. Results: The 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. Conclusions: We conclude that boosted regression trees can be used to uncover complex interaction effects in epidemiological studies.

Place, publisher, year, edition, pages
2014. Vol. 13, 57- p.
National Category
Probability Theory and Statistics Public Health, Global Health, Social Medicine and Epidemiology
URN: urn:nbn:se:uu:diva-228915DOI: 10.1186/1476-069X-13-57ISI: 000340001300001PubMedID: 24993424OAI: oai:DiVA.org:uu-228915DiVA: diva2:735087
Available from: 2014-07-22 Created: 2014-07-22 Last updated: 2015-03-09Bibliographically approved
In thesis
1. Mixture Effects of Environmental Contaminants
Open this publication in new window or tab >>Mixture Effects of Environmental Contaminants
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Chemical exposure in humans rarely consists of a single chemical. The everyday exposure is characterized by thousands of chemicals mainly present at low levels. Despite that fact, risk assessment of chemicals is carried out on a chemical-by-chemical basis although there is a consensus that this view is too simplistic.

This thesis aims to validate a statistical method to study the impact of mixtures of contaminants and to use that method to investigate the associations between circulating levels of a large number of environmental contaminants and atherosclerosis and the metabolic syndrome in an elderly population. Contaminants measured in the circulation represented various classes, such as persistent organic pollutants, plastic-associated chemicals and metals.

There was little co-variation among the contaminants and only two clusters of PCBs could be discerned. Gradient boosted CARTs were used to assess additive and multiplicative associations between atherosclerosis, as measured by the intima-media thickness (IMT) and the echogenicity of the intima-media complex (IM-GSM), and prevalent metabolic syndrome.

Systolic blood pressure was the most important predictor of IMT while the influence of the contaminants was marginal. Three phthalate metabolites; MMP, MEHP and MIBP were strongly related to IM-GSM. A synergistic interaction was found for MMP and MIBP, and a small antagonistic interaction was found for MIBP and MEHP. Associations between the contaminants and prevalent metabolic syndrome were modest, but three pesticides; p,p’-DDE, hexachlorbenzene and trans-nonachlor along with PCBs 118 and 209 and mercury were the strongest predictors of prevalent metabolic syndrome.

This thesis concludes that many contaminants need to be measured to get a clear picture of the exposure. Boosted CARTs are useful for uncovering interactions. Multiplicative and/or additive effects of certain contaminant mixtures were found for atherosclerosis or the metabolic syndrome.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 85 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1059
Mixtures, Environmental Contaminants, Atherosclerosis, Metabolic Syndrome, Boosting, Epidemiology
National Category
Environmental Health and Occupational Health Probability Theory and Statistics
urn:nbn:se:uu:diva-237690 (URN)978-91-554-9122-2 (ISBN)
Public defence
2015-02-06, Frödingsalen, Ulleråkersvägen 40A, Uppsala, 09:00 (Swedish)
Available from: 2015-01-16 Created: 2014-12-04 Last updated: 2015-03-09

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