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.
This is an online course meant to help researchers design and analyze trials involving groups or clusters.
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.
The series provides an overview of analytic approaches, methods, and statistical applications for analyzing tobacco regulatory science (TRS) data. The presenters include an esteemed group of scientists, well known for their work in methodological research dealing with casual inference. The webinars are intended for any investigator funded by the Center for Tobacco Products.
The National Cancer Institute is hosting this training institute to provide participants with a thorough grounding in conducting D&I research with a specific focus on cancer, across the cancer control continuum. In 2020, the institute will use a combination of online coursework (six modules with related assignments) and a 2-day in-person training to be held August 3 and 4, 2020, at the NCI campus in Bethesda, MD. Faculty and guest lecturers consist of leading experts in D&I theories, models, and frameworks; intervention fidelity and adaptation; stakeholder engagement and partnership for D&I; research methods and study designs for D&I; and measures and outcomes for D&I. This training institute has been adapted from the broader Training Institute for Dissemination and Implementation Research in Health (TIDIRH), organized by NIH and the VA over the past nine years.
This training is designed for investigators at any career stage interested in conducting D&I research with a focus on the cancer control continuum. There is no cost associated with the training. Invited participants are required to cover related travel expenses to the Washington D.C. area for the in-person meeting. More answers to common questions can be found on the site FAQ.
Dr. Laura Damschroder’s webinar introduces Consolidated Framework for Implementation Research (CFIR) and its application in a series of studies highlighting its use to guide data collection, analyses, and its potential for syntheses; and to guide tailoring of implementation strategies.
Dr. Greg Aarons’ webinar introduces the Exploration, Preparation, Implementation, Sustainment (EPIS) framework and its application in a series of studies highlighting its use to guide data collection, analyses, and its potential for syntheses; and to guide tailoring of implementation strategies.
Dr. Abe Wandersman’s webinar continued a series of presentations and discussions about the development and application of frequently-used implementation research models and frameworks. Dr. Wandersman, key developer of the Interactive Systems Framework (ISF), discusses the genesis of the framework, key terms and concepts, and then presents projects that have used the ISF as a core lens to support planning and study of evidence-based practice implementation.
Implementation science methodologies, approaches, and tools have a great interdisciplinary applicability. Dr. Alice Ammerman’s webinar discusses what new (and "new to") D&I investigators need to know to succeed in this burgeoning field.