A Systematic Comparison of Linear Regression-based Statistical Methods to Assess Exposome-Health Associations
Roel VERMEULEN, Utrecht University, Netherlands
SIROUX V. 1
, GIORGIS- ALLEMAND L. 1
, BASAGAÑA X. 4,5,6
, CHADEAU-HYAM M. 3
, PORTENGEN L. 2
, AGIER L. 1
, SLAMA R. 1
, VRIJHEID M. 4,5,6
, VINEIS P. 3
, NIEUWENHUIJSEN M. 4,5,6
, GONZÁLEZ J. 4,5,6,
, VLAANDEREN J. 2
, ROBINSON O. 4,5,6
1 Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Inserm 9 and Univ. Grenoble-Alpes, U823 Joint Research Center, Grenoble, France
2 Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
3 Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, United 13 Kingdom
4 Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
5 Universitat Pompeu Fabra (UPF), Barcelona, Spain
6 CIBER Epidemiología y Salud Pública (CIBERESP), Spain
The exposome constitutes a promising framework to better understand the effect 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 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 a FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm a sensitivity 49 of 80% and a FDP of 33%. 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 are limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. While 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