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 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 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.
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.
This is an online course meant to help researchers design and analyze trials involving groups or clusters.
Research Domain Criteria (RDoC) is a research framework for new ways of studying mental disorders. It integrates many levels of information (from genomics to self-report) to better understand basic dimensions of functioning underlying the full range of human behavior from normal to abnormal.
RDoC Educational and Training Resources page includes links to RDoC office hours, webinars, and RDoC-influenced courses.
Dr. Doug Luke provides a general overview of agent-based modeling (ABM) methods, and then discusses in more detail the utility of these methods for studying the design and implementation of new policies and practices related to chronic diseases, including obesity and tobacco control. The specific advantages of ABMs for dissemination and implementation science are also highlighted.
Dr. Amy Kilbourne introduces the SMART design as well as other adaptive design variations to inform the development of adaptive interventions. Dr. Kilbourne explains the use of the designs in intervention trials, walks through their applicability to implementation studies, discusses differences between adaptive designs and adaptive interventions, and concludes with examples from her work of how adaptive designs have permitted the testing of implementation strategies.
Part one of the two-part series, Measuring Success in Low-Income Nutrition Education and Obesity Prevention Programs, explores how to use the framework to evaluate nutrition education and obesity prevention programs.
Part two, Strategies and Tools for Measuring the Priority Indicators, highlights the seven SNAP-Ed priority indicators from the Evaluation Framework and shares practical examples of measuring healthy eating behaviors, physical activity, and reduced sedentary behaviors in low-income children and families.
The objective of this course is to provide a thorough grounding in the conduct of randomized clinical trials to researchers and health professionals interested in developing competence in the planning, design, and execution of randomized clinical trials involving behavioral interventions.
The curriculum will enable participants to:
- Describe the principles underlying the conduct of unbiased clinical trials
- Identify the unique challenges posed by behavioral randomized clinical trials (RCTs)
- Evaluate RCT designs in terms of their appropriateness to scientific and clinical goals
- Select appropriate strategies for enrollment, randomization, and retention of participants
- Understand methods for monitoring, coordinating, and conducting RCTs
- Develop strategies for appropriate statistical analyses of RCT data
- Evaluate the quality of behavioral RCTs and interpret their results
- Design an RCT as part of a working group on a specific topic.
Dr. Sterman discusses systems approaches in public health, including the concepts of policy resistance, implementation feedbacks, and model boundaries and explores how these ideas can be applied to effect change in a complex system. He includes examples from healthcare and public health such as implementation of formulary drug lists and SARS epidemic modeling.
Dr. McLeroy discusses adoption of systems methodology, including multiple levels of analysis, utility for identifying points of change, testing models against reality, and applications to program evaluation and various research designs, including community-based participatory research and randomized clinical trials.
Systems Network Analysis: Using Connections and Structures to Understand and Change Health Behaviors
Dr. Faust presents a non-technical overview of methods used to analyze networks, with an emphasis on social networks. Topics include: formal representations of social networks (graphs and sociomatrices), social network data considerations, and methods for analyzing social networks (connectivity, centrality, cohesive subgroups, equivalences and blockmodels, subgraphs, and structural hypotheses).
Dr. Valente describes methods for using network analysis to elucidate the antecedents and consequences of health-related behaviors. To do this, he draws from a number of examples of his applied work in the areas of substance abuse prevention and treatment, contraceptive choices, and community coalitions, among others. He also describes how applied research utilizing network analysis methods can be used to stimulate improvement in individual, community, and organizational behavior change programs.
This series of online lectures covers a range of diverse topics in data science such as data management, data representation, computing, data modeling, and other overarching topics. This series is an introductory overview that assumes no prior knowledge or understanding of data science.