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The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees.

Lampa E, Lind L, Lind PM, Bornefalk-Hermansson A - Environ Health (2014)

Bottom Line: There is a need to evaluate complex interaction effects on human health, such as those induced by mixtures of environmental contaminants.The simulated outcome contains one four-way interaction, one non-linear effect and one interaction between a continuous variable and a binary variable.Some spurious interactions were also found, however.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, 75185 Uppsala Sweden. erik.lampa@medsci.uu.se.

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.

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Related in: MedlinePlus

Interactions for SNR = 2. Black dots represent observed values of H, and boxes represent the  distributions H0. Small tick marks represent values of the  distribution below or above the 5th and 95th percentiles respectively.
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Figure 2: Interactions for SNR = 2. Black dots represent observed values of H, and boxes represent the distributions H0. Small tick marks represent values of the distribution below or above the 5th and 95th percentiles respectively.

Mentions: The top left panel of Figure 2 shows the strengths of the total interaction effects involving each of the ten most influential variables for SNR = 2. Dots are observed values of H and boxes represent the derived distributions of H for each variable. We see that p-p’-DDE, PCB 170, BPA, Cd, MMP and sex all seem to be involved in interactions, as the observed values of H are well outside the distribution, whereas OCDD, though it is an important variable, does not seem to interact with any other of the top ten variables.


The identification of complex interactions in epidemiology and toxicology: a simulation study of boosted regression trees.

Lampa E, Lind L, Lind PM, Bornefalk-Hermansson A - Environ Health (2014)

Interactions for SNR = 2. Black dots represent observed values of H, and boxes represent the  distributions H0. Small tick marks represent values of the  distribution below or above the 5th and 95th percentiles respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4120739&req=5

Figure 2: Interactions for SNR = 2. Black dots represent observed values of H, and boxes represent the distributions H0. Small tick marks represent values of the distribution below or above the 5th and 95th percentiles respectively.
Mentions: The top left panel of Figure 2 shows the strengths of the total interaction effects involving each of the ten most influential variables for SNR = 2. Dots are observed values of H and boxes represent the derived distributions of H for each variable. We see that p-p’-DDE, PCB 170, BPA, Cd, MMP and sex all seem to be involved in interactions, as the observed values of H are well outside the distribution, whereas OCDD, though it is an important variable, does not seem to interact with any other of the top ten variables.

Bottom Line: There is a need to evaluate complex interaction effects on human health, such as those induced by mixtures of environmental contaminants.The simulated outcome contains one four-way interaction, one non-linear effect and one interaction between a continuous variable and a binary variable.Some spurious interactions were also found, however.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, 75185 Uppsala Sweden. erik.lampa@medsci.uu.se.

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.

Show MeSH
Related in: MedlinePlus