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625 - METAL MIXTURES AND SARCOPENIA-RELATED OUTCOMES IN OLDER ADULTS: A BAYESIAN KERNEL MACHINE REGRESSION STUDY IN THE ENRICA COHORT

open access: yesGaceta Sanitaria
A. Santos-Cuerva   +8 more
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Prenatal Exposure to Endocrine Disrupting Chemical Mixtures and Infant Birth Weight: A Bayesian Analysis Using Kernel Machine Regression

Environmental Research, 2021
Pregnant women are regularly exposed to a multitude of endocrine disrupting chemicals (EDCs). EDC exposures, both individually and as mixtures, may affect fetal growth. The relationship of EDC mixtures with infant birth weight, however, remains poorly understood.
Aimin Chen   +2 more
exaly   +3 more sources

The Sensitivity of Bayesian Kernel Machine Regression (BKMR) to Data Distribution: A Comprehensive Simulation Analysis

open access: yesJournal of Statistical Computation and Simulation
Bayesian Kernel Machine Regression (BKMR) has emerged as a powerful tool to detect negative health effects from exposure to complex multi-pollutant mixtures. However, its performance is degraded when data deviate from normality. In this comprehensive simulation analysis, we show that BKMR’s power and test size vary under different distributions and ...
Hasan, Kazi Tanvir   +3 more
exaly   +3 more sources

Bayesian Kernel Machine Regression for Social Epidemiologic Research

Epidemiology
Background: Little attention has been devoted to framing multiple continuous social variables as a “mixture” for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects ...
Jemar R. Bather   +2 more
openaire   +2 more sources

Mixture Analyses on Polycyclic Aromatic Hydrocarbons: An Example Using Semen Quality Biomarkers and Bayesian Kernel Machine Regression

Polycyclic Aromatic Compounds, 2021
This study aimed to assess mixture effects of 16 targeted PAHs on male reproductive health by applying a novel grouping approach to the Bayesian kernel machine regression (BKMR) model.
Hueiwang Anna Jeng, Sinjini Sikdar
openaire   +1 more source

[Benchmark dose estimation of polycyclic aromatic hydrocarbons exposure base on Bayesian kernel machine regression].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases, 2023
Objective: To explore benchmark dose (BMD) estimations of polycyclic aromatic hydrocarbons (PAHs) based on Bayesian kernel machine regression (BKMR) . Methods: A total of 155 adult residents of a coking plant in Shanxi Province who were surveyed in summer (June to August) from 2014 to 2019 were selected as the research objects. Fasting elbow vein blood
Q Q, Wang   +5 more
openaire   +1 more source

Bayesian Kernel Machine Regression for Estimating the Prostate Cancer Risk of Heavy Metal Mixtures in the EPIC-Spain Cohort

2022
The association of exposure to several heavy metals with prostate cancer risk has been assessed in some studies, but the effects of complex interactions among heavy metals remain unclear. We aim to assess the association between metal mixtures exposure and prostate cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC ...
Karen dos Santos Gonçalves   +8 more
openaire   +1 more source

Mixture effects of multiple exposures on driving safety at tunnel entrances and exits: A Bayesian kernel machine regression (BKMR) method

Accident Analysis & Prevention
Tunnel entrances and exits are widely recognized as safety-critical zones due to abrupt environmental transitions. However, most prior work examines single domains (e.g., spatial or lighting) and often assumes linear, additive effects, leaving mixed, non-linear and interactive association patterns insufficiently understood.
Congcong Bai   +6 more
openaire   +2 more sources

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