We are exposed to numerous environmental chemicals each day. We are interested in quantifying the health effects of environmental chemical mixtures, assessing joint actions, and identifying the interactions of combined chemicals. Analyzing health effects of chemical exposures can contribute to preventive measures to mitigate the potential impact of these exposures.
In this project, we aim to summarize advanced statistical approaches for analysis of complex mixtures and knit them to R tutorials to make them accessible to researchers without extensive statistics or mathematics backgrounds. This will include online tutorials to introduce advanced statistical approaches to scientists and to provide examples of their use using national survey data.
In 2015, the National Institute of Environmental Health Sciences (NIEHS) convened a workshop to bring together experts to “identify and compare statistical approaches for analyzing chemical mixture data in epidemiological studies”. The collection of abstract, code, and datasets from the workshop are all available on the NIEHS website. NIEHS also launched a funding initiative to address the analytical challenges of environmental mixtures research, called Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program. Various new statistical methods supported by the NIEHS PRIME Program are available in Github.
This research project is funded by the Superfund Duke.
The Superfund Research Center at Duke University focuses on early, low-dose exposures to environmental contaminants and their developmental impacts, changes usually only evident later in life. Research at the Center has shifted during the 20 years of funding, as knowledge of Superfund chemicals and remediation techniques have improved. Though the basic research questions have changed, the focus on developmental impacts of environmental toxicants has been a constant.