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. Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. This paper describes a simulation study in an exposome context, comparing the performance of several statistical methods that have been proposed to detect statistical interactions.
Simulations were based on an exposome including 237 exposures with a realistic correlation structure. The statistical regression-based methods used include 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). GLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.
