Click here to download a PDF copy of the project description.
Team Members
Instructors
| Name | Contact | Location | Role |
|---|---|---|---|
| Prof. Amy Herring | amy.herring@duke.edu | Old Chem 208 | Supervisor |
| Yunran Chen | yunran.chen@duke.edu | Old Chem 022 | Mentor |
Research Assistants
| Name | Contact | Role | |
|---|---|---|---|
| Olivia Fan | zimeng.fan@duke.edu | Research Assistant | |
| Ryan Mitchell | ryan.mitchell@duke.edu | Research Assistant | |
| Bradley Bowen | bradley.bowen@duke.edu | Research Assistant |
Project Meetings
| Day | Time | Location | |
|---|---|---|---|
| Meetings | Wednesdays | 9:15am-9:45am | Old Chem 208 |
| Mondays | 3:30pm-4:00pm | Old Chem 208 |
Project Description
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. A possible side project, depending on skills and interests, includes improving workflow using virtual lab notebooks for data collection and annotation.
The tutorials will mainly be based on materials from NIEHS 2015 workshop and PRIME program. This research project is supported by Superfund Research Center at Duke University.
NIEHS 2015 workshop
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, codes, and datasets from the workshop are all available on the NIEHS website.
PRIME Program
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.
Project Objectives
In this project, you will …
- learn advanced statistical approaches on analyzing chemical mixtures
- gain a working knowledge of these methods
- be able to communicate them with people without extensive statistics or mathematics background
- be familiar with the R language so that you will be able to apply these methods to answer scientific questions using R
- use R Markdown to write reproducible reports and GitHub for version control and collaboration
By the end of the semester, we will have …
- an online tutorial (this website) introducing statistical approaches for chemical mixtures analysis and applications using R
- For students interested in research, we may consider a new research project focusing on developing statistical models and methods for environmental chemical mixtures
- A possible side project, depending on skills and interests, includes improving workflow using virtual lab notebooks for data collection and annotation.
Instructions on uploading your tutorials
- Make sure you have the R project linked to the repo.
- Upload your .Rmd/.qmd file to the folder: static -> slides
- Knit your file to .html
- Edit the . yaml file: data/schedule.yaml. Put the name of your uploaded tutorial after
ae: