Background: The exposome constitutes a promising framework to improve understanding of the effects of environmental exposures on health by explicitly considering multiple testing and avoiding selective reporting. However, exposome studies are challenged by the simultaneous consideration of many correlated exposures.
Objectives: We compared the performances of linear regression–based statistical methods in assessing exposome-health associations.
Methods: In a simulation study, we generated 237 exposure covariates with a realistic correlation structure and with a health outcome linearly related to 0 to 25 of these covariates. Statistical methods were compared primarily in terms of false discovery proportion (FDP) and sensitivity.
Results: On average over all simulation settings, the elastic net and sparse partial least-squares regression showed a sensitivity of 76% and an FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm revealed a sensitivity of 81% and an FDP of 34%. The environment-wide association study (EWAS) underperformed these methods in terms of FDP (average FDP, 86%) despite a higher sensitivity. Performances decreased considerably when assuming an exposome exposure matrix with high levels of correlation between covariates.
Conclusions: Correlation between exposures is a challenge for exposome research, and the statistical methods investigated in this study were limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. Although GUESS and DSA provided a marginally better balance between sensitivity and FDP, they did not outperform the other multivariate methods across all scenarios and properties examined, and computational complexity and flexibility should also be considered when choosing between these methods.
1Team of Environmental Epidemiology, Inserm, CNRS, University Grenoble-Alpes, IAB (institute for Advanced Biosciences), Grenoble, France; 2Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; 3Department of Epidemiology and Biostatistics, MRC-PHE (Medical Research Council–Public Health England) Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom; 4ISGlobal, Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; 5Universitat Pompeu Fabra (UPF), Barcelona, Spain; 6CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
Recommended Citation:
Lydiane Agier,1* Lützen Portengen,2* Marc Chadeau-Hyam,et al. A Systematic Comparison of Linear Regression–Based Statistical Methods to Assess Exposome-Health Associations[J]. Environmental Health Perspectives,2016-01-01,Volume 124(Issue 12):1848