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.
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 Methods: Mind the Gap presentation, Dr. Miller discusses the role of geospatial technologies and data in facilitating quasi and natural experiments about built environment factors that encourage active living. He also extends this idea to the concept of geographic information observatories: systems for ongoing data collection and analysis that facilitate opportunistic science that can leverage real-world events via ongoing observation, experimentation, and decision-support.
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.
The objective of this FAES Graduate School course is to provide an introduction to the principles and methods of epidemiology, defined as the study of the distribution and determinants of disease in populations. Lectures, problem sets, and outside reading will cover ecologic, case-control, cohort, and experimental studies. Topics to be discussed will include study design, measures of disease risk, sources of bias, methods of controlling for extraneous factors, principles of screening, and interpretation of data. Illustrations will include classic and contemporary examples in acute and chronic disease.
This is the first part of a two-part course. Registration is required separately for each part of the course.
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).
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.
In this Methods: Mind the Gap presentation, Dr. Dempsey focuses on mHealth studies in which both longitudinal and time-to-event data are recorded per participant. From assessing levels of biomarker association with event risk, to defining risk strata for a stratified micro-randomized trial, to post-study analysis of the treatment effect on event risk, he discusses how joint models enter into various stages of the intervention development process. He also discusses how mHealth studies present novel methodological challenges for joint modeling and solutions in several case studies. In each instance, he connects the joint modeling perspective back to how scientists can use them to inform multi-stage decision making in mHealth.
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).
Mixing qualitative and quantitative research methods can provide deeper exploration of causal mechanisms, interpretation of variables, and contextual factors that may mediate or moderate the topic of study. In this Methods: Mind the Gap webinar, Dr. Leonard Jason provides an introduction to the different approaches used in conducting mixed-methods research, including the benefits and challenges.
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 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 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.
Clinical trial guidelines have remained similar for decades despite long timelines, high costs, and frequent failures. This forum, convened by the NIH Scientific Program and Review Interest Group (SPRIG), addresses alternative strategies and models for clinical trials.
The speakers address the following questions:
- What are some potential advantages of group-randomized clinical trials?
- How can we mitigate the impact of non-adherence and professional subjects?
- How receptive is the FDA to novel trial strategies?
- Might artificial intelligence better guide future clinical trials?
In this Methods: Mind the Gap webinar, Dr. Stephen J. Mooney discusses approaches and useful tools for constructing measures of place from secondary data, including “ecometrics” (using psychometric techniques to build place-based scales), spatial interpolation, and assessing and correcting for sampling biases in crowdsourced data.