In this Methods: Mind the Gap webinar, Dr. Karina Davidson discusses single-patient trial design. The most scientifically rigorous—and potentially efficient—method for determining optimal clinical care for a specific patient is a single-patient (N-of-1) randomized controlled trial, in which data are collected objectively, continuously, and in the real world for a sufficient time period to determine whether the intervention, compared to a placebo, another intervention, or a different type of delivery or schedule, is optimal for that particular patient. With sufficient data from several N-of-1 trials of the same design, we can engage in inductive phenotype identification, but N-of-1 trials are only useful under certain circumstances.
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.
This webinar describes the purpose and use of the latest mobile-friendly version of the Automated Self-Administered 24-Hour Recall/Record System (ASA24). The tool is freely available to researchers, clinicians, and educators for dietary intake collection. Speakers describe the background of the tool, its evaluation, and best practices for its use.
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.
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?
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.
Missing data present a common problem for prevention research and improperly handling missing data can severely compromise the validity of a study’s inferences. In this Methods: Mind the Gap webinar, Dr. Little highlights the power and utility of modern principled treatments for missing data to optimize inferences. He discusses the three issues that occur when data go missing and the three mechanisms that give rise to missing data.
The problem of overdiagnosis, the detection by screening of latent cancers that would never have surfaced, has been much in the news lately. What is overdiagnosis, and how significant is the problem? In this Methods: Mind the Gap webinar, Dr. Etzioni examines how overdiagnosis arises and discusses what it takes to validly estimate its frequency.
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 a free, 7-part, self-paced online course with instructional slide sets, readings, and guided activities.
- Part 1 provides an introduction and overview of the three kinds of randomized trials and their distinguishing characteristics.
- Part 2 considers the design of group-randomized trials (GRTs).
- Part 3 discusses analytical approaches to GRTs and individually randomized group-treatment trials (IRGTs).
- Part 4 explores important power and sample size considerations for GRTs.
- Part 5 provides examples of GRTs from the Health Care Systems Collaboratory, a project funded by the NIH.
- Part 6 reviews recent practices in GRTs and IRGTs based on literature reviews.
- Part 7 examines alternative designs to evaluate interventions in comparison to GRTs and IRGTs.
In this Methods: Mind the Gap webinar, Dr. Linda Collins discusses why behavioral interventions are important in many areas of public health, for example, smoking cessation, drug abuse prevention, treatment of obesity, management of heart failure symptoms, and promotion of physical activity.
Behavioral interventions are typically developed and evaluated using a treatment package approach, in which the intervention is assembled a priori and evaluated by means of a randomized controlled trial (RCT). Dr. Collins reviews an alternative approach called the Multiphase Optimization Strategy (MOST), an engineering-inspired framework for developing, optimizing, and evaluating behavioral interventions. MOST includes the RCT, as well as other empirical steps aimed at intervention optimization.
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.
Interventions to change behavior have great potential to improve health and well-being. In this Methods: Mind the Gap webinar, Dr. Michie describes a method for specifying the "active ingredients" of interventions. Such a method allows data pooling to identify effective component techniques. She also presents a systematic method for developing interventions, linking a "behavioral diagnosis" of the behavior needing to be changed with general strategies and specific techniques.
Researching the Impossible: The Utility of Agent-Based Models for Advancing Public Health Policy and Implementation Science
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.
Risk Prediction Models for Breast Cancer in Black Women: The Importance of Considering Molecular Subtypes
In this Methods: Mind the Gap presentation, Dr. Palmer addresses the relatively low performance of risk prediction models for breast cancer in Black women versus performance in other populations, and possible reasons for the observed disparity.
Boston University School of Medicine
NCI has developed two online resources for use in selecting instruments for various research questions: the Dietary Assessment Primer and the Measures Registry. Both are designed to provide knowledge transfer and facilitate adoption of best assessment practices. This webinar describes each of these resources with a focus on how we can minimize measurement error by opening up access to the best assessment methods.