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.
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 archive provides a collection of webinars on methodology. The topics include HIV prevention, implementation methods, personalized medicine, complexity, and longitudinal data. In 2017, the Office of Disease Prevention (ODP) provided co-funding to the Center for Prevention Implementation Methodology to help create this archive.
A collection of training modules that came out of the NIH's initiative to enhance rigor and reproducibility in the research endeavor. The modules were developed by the NIH or NIH-funded grantees and focus on a variety of topics, including integrating sex into research, the design and analysis of group-randomized trials, and computational analyses.
This training is geared towards raising comprehension of fundamental data science processes and concepts across ten technical data science competencies: research design, programming and scripting, computer science, advanced math, database science, data mining and integration, statistical modeling, machine learning, operations research, and data visualization.
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.
The objective of this FAES Graduate School is to provide a deeper understanding of epidemiologic research methodology that can be used to interpret critically the results of epidemiologic research. This understanding is the result of investigating conceptual models for study designs, disease frequency, measures of association and impact, imprecision, bias, and effect modification. The course emphasizes the interpretation of research, even when the design or execution of the respective research is less than ideal.
A collection of online chapters that provide an introduction to selected behavioral and social science research approaches, including theory development and testing, survey methods, measurement, and study design. eSource was developed in 2010, and these chapters have not been updated to reflect advances in the past decade. However, they can still be used as supplementary teaching materials.
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.
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.
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).
This course trains registrants on how to effectively and safely conduct clinical research. It focuses on the spectrum of clinical research and the research process by highlighting biostatistical and epidemiologic methods, study design, protocol preparation, patient monitoring, quality assurance, ethical and legal issues, and much more. This course will be of interest to physicians, scientists, medical and dental students, nurses, public health professionals, and others conducting or planning a career in clinical research.
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.
This week-long immersion program provides 30 selected investigators with a thorough introduction to selected mHealth methodologies that may be used to study behavioral and social dimensions of public health. Participants work with expert mentors to create their own inter-disciplinary mobile health projects.
The mHealth training institute is funded via the NIH BD2K Program. The NIH BD2K Program is funded by all the NIH Institutes and Centers and receives support from the NIH Common Fund and the NIH Office of Behavioral Health and Social Sciences Research (OBSSR).
The NINDS Clinical Trials Methodology Course (CTMC) is an intensive, engaging program designed to help junior investigators develop scientifically rigorous, yet practical clinical trial protocols, and to focus on early consideration of funding mechanisms as a key trial planning activity.
The NINR Big Data in Symptoms Research Boot Camp, part of the NINR Symptom Research Methodologies Series, is a one-week intensive research training course at the National Institutes of Health (NIH) in Bethesda, Maryland. It provides a foundation in methodologies for using Big Data in research. The purpose of the course is to increase the research capability of graduate students and faculty.
Motivated by the analysis of intensive care unit data, this talk discusses new methods to automatically extract causal relationships from data and how these have been applied to gain new insight into stroke recovery. Finally, the speaker discusses recent findings in cognitive science and how they can help us make better use of causal information for decision-making.
This course discusses machine learning (ML), which has become a core technology underlying many modern applications, especially in health care. Machine learning techniques provide powerful methods for analyzing large datasets such as medical images, electronic health records, and genomics. Recent advances in deep learning (DL) provide an analysis framework that can be used to automatically classify images and objects with (and occasionally exceeding) human-level accuracy.
The goal of this FAES course is to introduce biomedical research scientists to R as an analysis platform rather than a programming language. Throughout the course, emphasis is placed on example-driven learning. Topics include: installation of R and R packages; command line R; R data types; loading data in R; manipulating data; exploring data through visualization; statistical tests; correcting for multiple comparisons; building models; and generating publication-quality graphics. No prior programming experience is required.