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Environ Health. 2017 Jul 14;16(1):74. doi: 10.1186/s12940-017-0277-6.

A systematic comparison of statistical methods to detect interactions in exposome-health associations.

Author information

1
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Dr. Aiguader, 88, Barcelona, 08003, Spain.
2
Universitat Pompeu Fabra (UPF), Plaça de la Merçè, 10-12, Barcelona, 08002, Spain.
3
CIBER Epidemiología y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5 Pabellón 11. Planta 0, Madrid, 28029, Spain.
4
Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Inserm and University Grenoble Alpes, U823 Joint Research Center, Grenoble, France.
5
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands.
6
Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, W2 1PG London, UK.
7
MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
8
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Dr. Aiguader, 88, Barcelona, 08003, Spain. xavier.basagana@isglobal.org.
9
Universitat Pompeu Fabra (UPF), Plaça de la Merçè, 10-12, Barcelona, 08002, Spain. xavier.basagana@isglobal.org.
10
CIBER Epidemiología y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5 Pabellón 11. Planta 0, Madrid, 28029, Spain. xavier.basagana@isglobal.org.

Abstract

BACKGROUND:

There is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures from conception onwards). Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. We performed a simulation study in an exposome context to compare the performance of several statistical methods that have been proposed to detect statistical interactions.

METHODS:

Simulations were based on an exposome including 237 exposures with a realistic correlation structure. We considered several statistical regression-based methods, including two-step Environment-Wide Association Study (EWAS2), the Deletion/Substitution/Addition (DSA) algorithm, the Least Absolute Shrinkage and Selection Operator (LASSO), Group-Lasso INTERaction-NET (GLINTERNET), a three-step method based on regression trees and finally Boosted Regression Trees (BRT). We assessed the performance of each method in terms of model size, predictive ability, sensitivity and false discovery rate.

RESULTS:

GLINTERNET and DSA had better overall performance than the other methods, with GLINTERNET having better properties in terms of selecting the true predictors (sensitivity) and of predictive ability, while DSA had a lower number of false positives. In terms of ability to capture interaction terms, GLINTERNET and DSA had again the best performances, with the same trade-off between sensitivity and false discovery proportion. When GLINTERNET and DSA failed to select an exposure truly associated with the outcome, they tended to select a highly correlated one. When interactions were not present in the data, using variable selection methods that allowed for interactions had only slight costs in performance compared to methods that only searched for main effects.

CONCLUSIONS:

GLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.

KEYWORDS:

Exposome; Interactions; Variable selection

PMID:
28709428
PMCID:
PMC5513197
DOI:
10.1186/s12940-017-0277-6
[Indexed for MEDLINE]
Free PMC Article

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