This FAES course covers advanced SAS coding concepts such as the use of SAS Macro, SAS SQL, as well as a combination of both. The course also introduces students to SAS STAT coding for common statistical tests (such as t-test, ANOVA, linear regression, and others). Students have the opportunity to practice in class, using sample datasets. Homework and project assignments are provided as well.
The public funding of research includes many discrete components: setting research priorities; securing funds; funding research infrastructure; selecting and funding meritorious projects; conducting research; monitoring research progress; communicating research findings; and training researchers. This FAES survey course is deigned to review theories, methods, and practices in program and policy evaluation as they relate to research, particularly publicly funded biomedical research. The full range of the evaluation hierarchy (needs assessment and program planning, feasibility and implementation evaluation, process evaluation, and outcome and impact evaluation) is considered as students will be guided to develop a comprehensive framework for the evaluation of federally funded biomedical research.
This FAES course gives a broad and conceptual overview of the most popular machine learning algorithms, followed by examples of how and when to apply them to real data. Best practices in designing machine learning analyses will be emphasized and reviewed, along with how to avoid common pitfalls and how to interpret analysis results.
This FAES Graduate School course introduces students to the theory and practice of cancer screening in the United States. Students learn about the methodology used to assess cancer screening tests; how to interpret cancer screening data; and how to identify potential benefits and harms of cancer screening. They also become familiar with the evidence in favor of and against population-based screening for breast, colorectal, lung, cervical, and prostate cancer, as well as the controversies that surround mass screening for these diseases.
In this webinar, Drs. Maria Fernandez and Gregory Aarons discuss the development, submission, and review of grants and scientific publications from the perspective of both author and reviewer. They also touch on larger issues for the field including the scientific and technical aspects of career development in implementation science, focusing research efforts, and the landscape of traditional journals, open access, social media, and other mechanisms for communicating.
This FAES course demonstrates and practices the use of R in creating and presenting data visualizations. After a short introduction to R tools, especially the tidyverse packages, the course covers principles for data visualization, examples of good and bad visualizations, and the use of ggplot2 to create static publication-quality graphs. Students also have the chance to learn about modern web-based interactive graphics using the html widgets packages as well as dynamic graphics and dashboards that can be created using flexdashboard and Shiny. The course explores ways in which bioinformatics data can be presented using static and dynamic visualizations. Finally, RMarkdown and other packages are used to develop webpages for presenting data visualizations as self-explanatory and possibly interactive storyboards.
This webinar outlines successes, motivators, and challenges faced by early-stage investigators in the field. In response to audience feedback, the speakers touch on issues in implementation science, such as training, career development, and working with an active D&I funding portfolio with a focus on early and mid-career researchers.
From Purchase to Plate: Linking USDA Nutrition Data with Retail Scanner Data to Assess the Healthfulness of America’s Food-at-Home Purchases
On May 23, 2019, NCCOR hosted a Connect & Explore webinar to discuss the findings in a recent publication from the U.S. Department of Agriculture Economic Research Service called “Linking USDA Nutrition Databases to IRI Household-Based and Store-Based Scanner Data.” USDA researchers created a purchase-to-plate “crosswalk”—linking USDA data and household retail scanner data—to measure the overall healthfulness of American’s food-at-home (FAH) purchases. Results show that improvements in the healthfulness of Americans’ FAH purchases are needed to comply with federal dietary guidance. The speaker is Andrea Carlson, PhD, MS,an economist in the Food Markets Branch of the Food Economics Division.
Genomics and Health Disparities Lecture Series: Exploring the Role of Genomics in Achieving Health Equality
This lecture series was formed to enhance opportunities for dialogue about how innovations in genomics research and technology can impact health disparities. Topics range from basic science to translational research.
In this introductory FAES Graduate School class, students learn the foundations of health economics and econometric modeling and apply them to the evaluation of biomedical research and public health programs.
Each year, the federal government collects, manages, and makes available considerable amounts of population health data. In this course, students gain working knowledge of databases, such as NHANES, NHIS, and MEPS, that are frequently used by public health analysts, policy makers, and researchers. The course will cover the types of variables that are included in each database. It will also discuss how the data are collected, how to retrieve the data, and how to prepare the data for statistical analysis. Using SAS or STATA, students learn how to develop appropriate research questions and analyze the data, with emphasis on data management, exploratory data analysis, regression analysis, and the interpretation of statistical analysis. Finally, students will study a series of published papers on health policy in order to understand the application of statistical methods to the field.
This innovative program will place a strong emphasis on mentoring, applying competencies and curriculum specifically focused on chronic disease disparities, and working with a diverse set of partners. Scholars are enrolled in the program for two years.
This FAES course covers the basics of some tools that students can subsequently use to work with Data Science, such as Hadoop’s MapReduce, Apache Spark, Pig, Hive, Python, and R. In addition, the course will cover advanced data structures as well as real-world data scraping, cleansing, and wrangling. The course will also include a high-level overview of machine-learning concepts.
Python is a free, open-source and powerful programming language that is easy to learn. This FAES course is intended for nonprogrammers who want to learn how to write programs that expand the breadth and depth of their daily research. Most elementary concepts in modern software engineering are covered, including basic syntax, reading from and writing texts files, debugging python programs, regular expressions, and creating reusable code modules that are distributable to peers. The course also focuses on potential applications of Python to bioinformatics, including sequence analysis, data visualization, and data analysis. Students also learn to use the Jupyter Notebook and the PyCharm integrated development environment (IDE), which are available at no cost.
R is a free statistics software that is becoming increasingly popular and important for data analysis in biology. During this FAES course, students first learn how to handle the R programming environment. Next, students learn how to simulate data for analysis, while the background for R programming is provided in accompanying lectures. At the end of the course, students become familiar with simple R programming, which they can then apply to their own data analysis.
The course covers the fundamentals of the SAS program and its variables, creating data, importing data (from text and Excel files), exporting data (to text, pdf, and Microsoft-related formats), manipulating data, and providing descriptive statistics. Students have the opportunity to practice in class, using sample datasets. Homework and project assignments will be provided as well.
This FAES class aims to introduce fundamental subjects in text mining such as tokenization, named entity recognition (NER), grammars, parsing, relation extraction, and document classification. The class is oriented towards hands-on experience with Python and Natural Language Toolkit (NLTK).
The objective of this FAES Graduate School course is to learn the concepts and methodology used in the design and conduct of randomized clinical trials. Topics to be covered will include description of the main types of trial designs, principles of randomization and stratification, issues in protocol development (defining objectives and endpoints, blinding, choice of control), recruitment and retention, data collection and quality control issues, monitoring, and analyses of trials reports.
The NIH-Duke Master's Program in Clinical Research, established in 1998, is one of the nation's first training programs in clinical research. This program allows participants to attend formal courses in research design, research management, medical genomics, and statistical analysis at the Clinical Center by means of video-conferencing from Duke or on-site by adjunct faculty.
The program leads to a Master of Health Sciences in Clinical Research, a professional degree awarded by the Duke University School of Medicine. There is also a non-degree option for qualified students who want to pursue specific areas of interest.
Applications will be accepted through August 1, 2020.
The program, intended for early stage research investigators, features lectures, mock grant review, seminars, and small group discussions on research relevant to minority health and health disparities. It also includes sessions with NIH scientific staff engaged in related health disparities research across the various institutes and centers.
Lectures and seminars include:
- Population science and health disparities
- Research design and measurement approaches
- Intervention Science methods
- Healthcare disparities and outcomes research
- Community-based participatory research
- Grant writing and mock grant review.