title: The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees.
abstract: 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.
authors.affiliation: Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, 75185 Uppsala Sweden. erik.lampa@medsci.uu.se.
authors.forename: Erik
authors.initials: E
authors.lastname: Lampa
authors.forename: Lars
authors.initials: L
authors.lastname: Lind
authors.forename: P Monica
authors.initials: PM
authors.lastname: Lind
authors.forename: Anna
authors.initials: A
authors.lastname: Bornefalk-Hermansson
mesh_terms: D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D004785:Environmental Pollutants / Q000633:toxicity*; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis
chemical_list: D004785:Environmental Pollutants