Combining Ensemble Learning Techniques and G-Computation to Investigate Chemical Mixtures in Environmental Epidemiology Studies

Oulhote, Youssef; Bind, Marie‐Abèle; Coull, Brent A.; Patel, Chirag; Grandjean, Philippe

doi:10.1101/147413

Cited by 5 publications

(5 citation statements)

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“…Guided by the predictive modeling methods for constructing cumulative environmental exposure scores, 6,7 we used 2 independent datasets to, first, build a predictive model in the training dataset (the Work-package 6 of the European Network of National Networks studying Gene-Environment Interactions in Schizophrenia [EUGEI] 2 ) and, second, construct and test the ES in the validation dataset (the Genetic Risk and Outcome of Psychosis [GROUP] study 8 ). We examined the following widely evaluated environmental factors that we also recently investigated individually within the context of gene-environment interaction 9 : hearing impairment, winter birth, cannabis use, and childhood adversities (bullying, emotional, physical, and sexual abuse along with emotional and physical neglect).…”

Section: Approachmentioning

confidence: 99%

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Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

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Lage‐Castellanos

et al. 2019

Schizophrenia Bulletin

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Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic “risk” and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke’s R2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P = .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome.

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Section: Approachmentioning

confidence: 99%

“…GROUP-EUGEI investigators are: Behrooz Z. Alizadeh 1 , Therese van Amelsvoort 2 , Richard Bruggeman 1 , Wiepke Cahn 3,4 , Lieuwe de Haan 5 , Jurjen J. Luykx 3,6,7 , Ruud van Winkel 2,8 , Bart P.F. Rutten 2 , Jim van Os 2,3,9 Estimating Exposome Score for Schizophrenia…”

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Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

Risk

Pries

Lage‐Castellanos

et al. 2019

Schizophrenia Bulletin

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“…In 2014, Mauderly and coauthors (70, 71) performed a multiple additive regression tree analysis in a multipollutant air quality study in rodents. In 2017, Oulhote et al (74) combined ensemble learning (i.e., SuperLearner) and g- computation to estimate the effect of chemical mixtures. In 2018, Golan et al (44) discussed the big data paradigm.…”

Section: The Future Of Causal Modeling In Environmental Healthmentioning

confidence: 99%

Causal Modeling in Environmental Health

Bind

2019

Annu. Rev. Public Health

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The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.

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“…Analyses using regression models in observational studies are even trickier, as the use of regression without a careful design phase, in which one tries to uncover or approximate some underlying randomized experiment, can lead to incorrect conclusions (e.g., see Rubin, 2008). Despite this difficulty, regression models with interaction terms are commonly used to estimate the effects of multiple treatments, in particular in observational studies (Patel, Bhattacharya & Butte, 2010; Bobb et al, 2015; Oulhote et al, 2017; Valeri et al, 2017). For instance, Bobb et al (2015) consider a Bayesian kernel machine regression for estimating the health effects of multi‐pollutant mixtures.…”

Section: Introductionmentioning

confidence: 99%

Causal inference for multiple treatments using fractional factorial designs

Pashley

Bind

2022

Can J Statistics

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We consider the design and analysis of multi‐factor experiments using fractional factorial and incomplete designs within the potential outcome framework. These designs are particularly useful when limited resources make running a full factorial design infeasible. We connect our design‐based methods to standard regression methods. We further motivate the usefulness of these designs in multi‐factor observational studies, where certain treatment combinations may be so rare that there are no measured outcomes in the observed data corresponding to them. Therefore, conceptualizing a hypothetical fractional factorial experiment instead of a full factorial experiment allows for appropriate analysis in those settings. We illustrate our approach using biomedical data from the 2003–2004 cycle of the National Health and Nutrition Examination Survey to examine the effects of four common pesticides on body mass index.

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Combining Ensemble Learning Techniques and G-Computation to Investigate Chemical Mixtures in Environmental Epidemiology Studies

Abstract: Background: Although biomonitoring studies demonstrate that the general population

Cited by 5 publications

References 42 publications

Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study

Causal Modeling in Environmental Health

Causal inference for multiple treatments using fractional factorial designs

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