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.
Many instruments in HealthMeasures are based on item response theory (IRT). IRT is a family of mathematical models that assumes that responses on a set of items or questions are related to an unmeasured “trait”. An example of such a trait may be physical function. IRT models assume a person’s level on physical function (e.g., high vs. low) will predict that person’s probability of endorsing each specific item.
This 6-part webinar series provides an overview of physical activity as a multidimensional health behavior; an in-depth review of methods to measure active and sedentary behaviors by self-report; and an exploration of important issues when assessing physical activity in diverse populations.
These modules are designed to complement the Measures Registry and Measures Registry User Guides and assist researchers and practitioners with choosing the best measures across the four domains of the Measures Registry: individual diet, food environment, individual physical activity and physical activity environment.
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).
In this webinar, Dr. Larry Palinkas introduces the use of mixed method designs in research on three interrelated facets of evidence-based practices implementation: provider social networks, use of research evidence, and cultural exchange between researchers and practitioners. Dr. Palinkas explains the multiple strategies through which qualitative and quantitative research methods can converge, specifically highlighting their use within three funded research studies of implementation.
Measuring and projecting the economic burden associated with cancer and identifying effective policies for minimizing its impact are increasingly important issues for health care policymakers and health care systems at multiple levels.
Written by experts in health economics, epidemiology, health services research, health policy, and biostatistics, this publication highlights the multiple benefits of comparing patterns of cancer care, costs, and outcomes across health systems within a single country or across countries.
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.
In collaboration with other academic institutions, professional organizations, and funding agencies, the Implementation Science team coordinates and supports several training and educational activities, including a monthly webinar series, training programs, and an annual conference.
In this presentation, Dr. Gortmaker presents the latest findings from the Childhood Obesity Intervention Cost-Effectiveness Study (CHOICES) project. CHOICES is a collaborative modeling effort designed to evaluate the effectiveness, costs, and reach of interventions to reduce childhood obesity in the United States.
As part of the ODP’s 2012 Physical Activity and Disease Prevention Workshop: Identifying Research Priorities session #3: Measurement of Physical Activity Behavior, Dr. Intille discusses the devices that might be used to measure and study physical activity versus sedentary behavior.
The NIH Disaster Research Response Program (DR2) is the national framework for research on the medical and public health aspects of disasters and public health emergencies. The DR2 website, provided by the National Institute of Environmental Health Sciences and the National Library of Medicine, supports disaster science investigators by offering data collection tools, training and exercises, research protocols, disaster research news and events, and more.
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.
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 journal supplement summarizes and builds upon a workshop which convened researchers from diverse sectors and organizations. The supplement discusses current technologies for objective physical activity monitoring, provides recommendations for the use of these technologies, and explores future directions in the development of new tools and approaches.
It presents best practices for using physical activity monitors in population-based research, explores modeling of physical activity outcomes from wearable monitors, and discusses statistical considerations in the analysis of accelerometry-based activity monitor data. It also examines monitor equivalency issues and discusses current use and best practices for accelerometry with particular populations—children, older adults, and adults with functional limitations.
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.