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

# metadata.dc.description.abstract[].value
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.

# metadata.dc.relation.ispartof[].value
Environmental Health, 2017, vol. 16, num. 1, 13 p.

# metadata.dc.rights[].value
cc by (c) Barrera Gómez, José et al., 2017

# metadata.dc.source[].value
Articles publicats en revistes (ISGlobal)

# metadata.dc.title[].value
A systematic comparison of statistical methods to detect
                interactions in exposome-health associations