Bayesian variable selection for understanding mixtures in environmental exposures

Kowal, Daniel R.; Bravo, Mercedes A.; Leong, Man Chong; Bui, Alexander A. T.; Griffin, Robert J.; Ensor, Katherine B.; Miranda, Marie Lynn

doi:10.1002/sim.9099

Cited by 13 publications

(16 citation statements)

References 53 publications

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“…BVSM is a variable selection strategy using sparse summaries of a linear regression model. It is most useful when trying to select variables and provide uncertainty quantification for a linear model used to characterize the effects of exposure [34]. Kowal et al used this Bayesian regression model to identify social and environmental covariates important for predicting educational outcomes.…”

Section: Toxic Agent Identification (Variable Selection)mentioning

confidence: 99%

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Joubert

Kioumourtzoglou

Chamberlain

et al. 2022

IJERPH

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Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

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Section: Toxic Agent Identification (Variable Selection)mentioning

confidence: 99%

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Joubert

Kioumourtzoglou

Chamberlain

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

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“…Similarly, our approach may be extended for functional response variables in function-on-function regression. In addition, the decision analysis strategy may be altered to induce other point estimates, such as sparse or locally linear summaries, by varying the penalty term in (9).…”

Section: Discussionmentioning

confidence: 99%

“…The decision-analytic optimality of δλ in ( 10) is valid only for a fixed λ in (9), which controls the number of partitions (or steps) in the locally-constant approximation { δk } K k=1 .…”

Section: Posterior Summarization For Interpretable Sofrmentioning

confidence: 99%

“…Informally, A ε collects all approximations δλ for which the predictive performance matches or exceeds that of δλ min with at least ε probability under the BASOFR model M. Equivalently, λ ∈ A ε if and only if a lower (1 − ε) posterior prediction interval for D λ includes zero (Kowal, 2021b). The acceptable family has been applied for targeted prediction (Kowal, 2021b), variable selection (Kowal et al, 2021), subset selection (Kowal, 2021a), and selection in mixed effects models (Kowal, 2021c). By default, we select ε = 0.05; smaller values expand the acceptable family, but results are generally robust to moderate changes in ε (Kowal, 2021b;Kowal et al, 2021;Kowal, 2021a).…”

Section: Posterior Summarization For Interpretable Sofrmentioning

confidence: 99%

“…The acceptable family has been applied for targeted prediction (Kowal, 2021b), variable selection (Kowal et al, 2021), subset selection (Kowal, 2021a), and selection in mixed effects models (Kowal, 2021c). By default, we select ε = 0.05; smaller values expand the acceptable family, but results are generally robust to moderate changes in ε (Kowal, 2021b;Kowal et al, 2021;Kowal, 2021a).…”

Section: Posterior Summarization For Interpretable Sofrmentioning

confidence: 99%

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Bayesian adaptive and interpretable functional regression for exposure profiles

Gao¹,

Kowal²

2022

Preprint

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Pollutant exposures during gestation are a known and adverse factor for birth and health outcomes. However, the links between prenatal air pollution exposures and educational outcomes are less clear, in particular the critical windows of susceptibility during pregnancy. Using a large cohort of students in North Carolina, we study prenatal PM 2.5 exposures recorded at near-continuous resolutions and linked to 4th end-of-grade reading scores. We develop a locally-adaptive Bayesian regression model for scalar responses with functional and scalar predictors. The proposed model pairs a B-spline basis expansion with dynamic shrinkage priors to capture both smooth and rapidly-changing features in the regression surface. The local adaptivity is manifested in more accurate point estimates and more precise uncertainty quantification than existing methods on simulated data. The model is accompanied by a highly scalable Gibbs sampler for fully Bayesian inference on large datasets. In addition, we describe broad limitations with the interpretability of scalar-on-function regression models, and introduce new decision analysis tools to guide the model interpretation. Using these methods, we identify a period within the third trimester as the critical window of susceptibility to PM 2.5 exposure.

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Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment

Midya,

Alcala,

Rechtman

et al. 2023

Environ. Sci. Technol.

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A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical–chemical interactions associated with ASD.

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Bayesian variable selection for understanding mixtures in environmental exposures

Cited by 13 publications

References 53 publications

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Bayesian adaptive and interpretable functional regression for exposure profiles

Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment

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