Summer 2020 dates: Live-stream, online training August 10-11, 2020; 10:00am-5:00pm EDT
Traditionally, environmental health studies have focused on assessing risks related to a single pollutant at a time. This, however, does not reflect reality, since we are constantly exposed to multiple pollutants at once. Recently, there has been an increased interest in methods that allow researchers to assess exposures to many pollutants at a time. These methods are able to accommodate the high dimension of the exposure matrix, as well as the usually high correlation across exposures of interest.
This two-day intensive workshop will provide a rigorous introduction to multiple different techniques to analyze exposure to mixtures in environmental health. Led by a team of world experts in environmental health, epidemiology and statistics, many of whom have developed their own methods to analyze exposure to mixtures, the workshop will integrate seminar lectures with hands-on computer lab sessions to put concepts into practice. Emphasis will be given to supervised and unsupervised methods. Since the choice of method depends on the research question at hand, the workshop will conclude with a panel discussion on when each method presented is appropriate for use and for which research questions.
By the end of the workshop, participants will be familiar with the following topics:
-Principle Component Analysis (PCA)
-Factor Analysis (FA)
-Variable Selection (Lasso, elastic net)
-Bayesian Kernel (BKMR)
-Weighted Quantile Sum Regression (WQS)
-Emerging mixtures topics and novel extensions
Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.
PREREQUISITES AND REQUIREMENTS
There are three prerequisites to attend this workshop:
1.Each participant must have an introductory background in statistics
2.Each participant must be familiar with R.
3.Each participant must bring a laptop with R downloaded and installed prior to the first day of the workshop. R is available for free download and installation on Mac, PC, and Linux devices.
Brent Coull, PhD, Harvard T.H. Chan School of Public Health, Harvard University.
Chris Gennings, PhD, Research Professor and Biostatistics Division Director, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai. Dr. Gennings’ research program includes the development of Weighted Quantile Sum (WQS) regression, joint work with a dissertation student, a method that is robust to confounding concerns based on complex correlations among exposure to environmental mixtures. She is currently developing methods for nutritional and environmental exposures that estimate and evaluate regulatory guideline values for mixtures.
Jeff Goldsmith, PhD, Assistant Professor, Department of Biostatistics, Columbia University Mailman School of Public Health. Dr. Goldsmith's statistical research focuses on high-dimensional data, with particular emphasis on dimension reduction methods, and modeling health outcomes. In addition to environmental health, he works on physical activity quantification using accelerometers, and on motor control experiments involving kinematic data.
Marianthi-Anna Kioumourtzoglou, ScD, Assistant Professor, Department of Environmental Health Sciences, Columbia University Mailman School of Public Health. Dr. Kioumourtzoglou is an environmental engineer and environmental epidemiologist by training, with a research emphasis on air pollution exposures. Her research focuses on statistical issues related to environmental epidemiology, such as assessing exposure to environmental mixtures (chemical and non-chemical) in health studies, and quantifying and correcting exposure measurement error.
Training scholarships are available for the Environmental Mixtures Workshop.