{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Devick KL"],"funding":["NIEHS NIH HHS","NIMH NIH HHS","National Institutes of Health","NIH HHS"],"pagination":["860-876"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9150437"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(5)"],"pubmed_abstract":["Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate natural direct and indirect effects and controlled direct effects of a complex mixture exposure on an outcome through a mediator variable. We implement Bayesian Kernel Machine Regression (BKMR) to allow for all possible interactions and nonlinear effects of (1) the co-exposures on the mediator, (2) the co-exposures and mediator on the outcome, and (3) selected covariates on the mediator and/or outcome. From the posterior predictive distributions of the mediator and outcome, we simulate counterfactuals to obtain posterior samples, estimates, and credible intervals of the mediation effects. Our simulation study demonstrat"],"journal":["Statistics in medicine"],"pubmed_title":["Bayesian kernel machine regression-causal mediation analysis."],"pmcid":["PMC9150437"],"funding_grant_id":["ES023515","P30 ES000002","R01 ES028800","ES015533","K01 MH118477","ES016454","K01MH118477","ES000002","ES028800","ES28522","R01 ES013744","T32 ES007142","U2C ES026555","R01 ES015533","K23 ES017437","P30 ES023515","P42 ES016454","ES026555","ES013744","ES007142","ES017437","R24 ES028522"],"pubmed_authors":["Devick KL","Bellinger DC","Williams PL","Valeri L","Bobb JF","Mazumdar M","Christiani DC","Coull BA","Claus Henn B","Wright RO"],"additional_accession":[]},"is_claimable":false,"name":"Bayesian kernel machine regression-causal mediation analysis.","description":"Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate natural direct and indirect effects and controlled direct effects of a complex mixture exposure on an outcome through a mediator variable. We implement Bayesian Kernel Machine Regression (BKMR) to allow for all possible interactions and nonlinear effects of (1) the co-exposures on the mediator, (2) the co-exposures and mediator on the outcome, and (3) selected covariates on the mediator and/or outcome. From the posterior predictive distributions of the mediator and outcome, we simulate counterfactuals to obtain posterior samples, estimates, and credible intervals of the mediation effects. Our simulation study demonstrat","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-04T09:41:25.319Z","creation":"2025-04-04T09:41:25.319Z"},"accession":"S-EPMC9150437","cross_references":{"pubmed":["34993981"],"doi":["10.1002/sim.9255"]}}