Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression

Background Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable...

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Veröffentlicht in:	Environmental health Jg. 17; H. 1; S. 67 - 10
Hauptverfasser:	Bobb, Jennifer F., Claus Henn, Birgit, Valeri, Linda, Coull, Brent A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 20.08.2018 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Air pollution Bayes Theorem Bayesian analysis Computer programs Continuity (mathematics) Dose-response effects Earth and Environmental Science Environment Environmental Epidemiology Environmental Exposure - adverse effects Environmental Health Environmental Health - methods Environmental Monitoring - methods Environmental Pollutants - adverse effects Epidemiology Estimation Exposure Exposure-response Gaussian process Health Health aspects Health risk estimation Medical research Methodology Mixtures Models, Statistical Multiple exposures Nonlinear response Occupational Medicine/Industrial Medicine Open source software Organic chemicals Pollutants Pollution Principal components analysis Programming languages Public Health Regression analysis Response functions Risk analysis Risk factors Simulation Software Source code Statistical analysis Statistical methods Statistical software Variable selection Exposure-response Mixtures Health risk estimation Multiple exposures Variable selection
ISSN:	1476-069X, 1476-069X
Online-Zugang:	Volltext
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