Subgroup analyses and effect modification with Bayesian kernel machine regression. [PDF]
Am J EpidemiolAbstract There is substantial interest in estimating the health effects of exposure to environmental mixtures. Bayesian kernel machine regression (BKMR) has emerged as a popular tool for mixture analyses. The health effects of environmental exposures, including mixture exposures, often differ among subpopulations.
Demateis D +3 more
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AtmosphereHigh blood pressure (BP) is a significant risk factor for heart and brain diseases. Previous studies have suggested that heavy metals including lead (Pb), mercury (Hg), and cadmium (Cd) contribute to hypertension. This study examined the combined effects
In-Gwon Kim +9 more
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Combined effects of multiple metals on hearing loss: A Bayesian kernel machine regression approach
Ecotoxicology and Environmental Safety, 2022 Background: Exposure to heavy elements is considered one of the risks of hearing loss. The combined effect of multiple metals on different hearing losses has not been extensively studied.
Mingming Liang +8 more
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Generalized Bayesian kernel machine regression
Statistical Methods in Medical ResearchKernel machine regression is a nonparametric regression method widely applied in biomedical and environmental health research. It employs a kernel function to measure the similarities between sample pairs, effectively identifying significant exposures and assessing their nonlinear impacts on outcomes. This article introduces an enhanced framework, the
Xichen Mou +2 more
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Bayesian kernel machine regression-causal mediation analysis. [PDF]
Stat Med, 2022 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
Devick KL +9 more
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The Association of Combined Per- and Polyfluoroalkyl Substances and Metals with Allostatic Load Using Bayesian Kernel Machine Regression. [PDF]
Diseases, 2023 Background/Objective: This study aimed to investigate the effect of exposure to per- and polyfluoroalkyl substances (PFAS), a class of organic compounds utilized in commercial and industrial applications, on allostatic load (AL), a measure of chronic stress.
Bashir T, Obeng-Gyasi E.
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Variational Bayesian multinomial probit regression with Gaussian process priors [PDF]
, 2006 It is well known in the statistics literature that augmenting binary and polychotomous response models with Gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior.
Rogers, S., Girolami, M.
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Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression. [PDF]
Int J Environ Res Public Health, 2023 This study aims to investigate the combined effects of per- and polyfluoroalkyl substances (PFAS) on allostatic load, an index of chronic stress that is linked to several chronic diseases, including cardiovascular disease and cancer. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007–2014, this study examines the ...
Bashir T, Obeng-Gyasi E.
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Impact of multi-heavy metal exposure on renal damage indicators in Korea: An analysis using Bayesian Kernel Machine Regression. [PDF]
Medicine (Baltimore), 2023 Exposure to cadmium (Cd), arsenic (As), and mercury (Hg) is associated with renal tubular damage. People living near refineries are often exposed to multiple heavy metals at high concentrations. This cross-sectional study investigated the association between combined urinary Cd, As, and Hg levels and renal damage markers in 871 residents living near ...
Choi SH, Choi KH, Won JU, Kim H.
europepmc +3 more sources
Assessment of Support Vector Machine performance for default prediction and credit rating [PDF]
Banks and Bank Systems, 2022 Predicting the creditworthiness of bank customers is a major concern for banking institutions, as modeling the probability of default is a key focus of the Basel regulations.
Karim Amzile, Mohamed Habachi
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