In this Methods: Mind the Gap presentation, Dr. MacKinnon describes mediation analysis and the connections between traditional mediation analysis and recently developed causal mediation analysis.
This training is geared towards raising comprehension of fundamental data science processes and concepts across ten technical data science competencies: research design, programming and scripting, computer science, advanced math, database science, data mining and integration, statistical modeling, machine learning, operations research, and data visualization.
This FAES course demonstrates and practices the use of R in creating and presenting data visualizations. After a short introduction to R tools, especially the tidyverse packages, the course covers principles for data visualization, examples of good and bad visualizations, and the use of ggplot2 to create static publication-quality graphs. Students also have the chance to learn about modern web-based interactive graphics using the html widgets packages as well as dynamic graphics and dashboards that can be created using flexdashboard and Shiny. The course explores ways in which bioinformatics data can be presented using static and dynamic visualizations. Finally, RMarkdown and other packages are used to develop webpages for presenting data visualizations as self-explanatory and possibly interactive storyboards.
During this webinar, Dr. Niven provides an overview of work on de-implementation while Dr. Norton provides cancer specific examples and insights. The session includes approximately 25 minutes of comments from the speaker and 35 minutes for engaged discussion and Q&A with the audience.
This one-day workshop explores challenges and strategies for design and analysis of embedded pragmatic clinical trials (PCT) that are conducted within health care systems.
In this Methods: Mind the Gap webinar, Dr. David M. Murray reviews the options available to evaluate multilevel interventions, including group- or cluster-randomized trials, and discusses their strengths and weaknesses.
In this Methods: Mind the Gap webinar, Dr. Melody S. Goodman discusses her efforts to develop and validate quantitative measures of stakeholder engagement in research and research literacy. Emerging data suggest a valid and reliable measure to accurately assess associations between research outcomes and stakeholder engagement. Data on the measure of research literacy show mixed results and Dr. Goodman discusses potential areas for modification.
In this Methods: Mind the Gap webinar, participants learn what the field of dissemination and implementation (D&I) is, why it is important, what it is trying to achieve, and how it is relevant to research and practice. Dr. Fernandez discusses the major components of a D&I study, D&I theories, models and frameworks, and design considerations. She also teaches participants how to tailor their own research to better enhance its value for dissemination and implementation.
This purpose of this Methods: Mind the Gap webinar is to equip public health researchers and practitioners with awareness and confidence in approaching and conducting qualitative research projects, and to familiarize participants with qualitative data collection and data analysis techniques and tools.
In this Methods: Mind the Gap presentation, Dr. Jeffrey Sparks illustrates how epidemiologic and patient-oriented research studies can further the understanding of etiology and outcomes of rheumatoid arthritis (RA), a common chronic disease. Different study designs are needed to investigate different types of exposures and outcomes. This presentation discusses studies related to lifestyle factors, genetics, biomarkers, comorbid conditions for RA risk, and outcomes, focusing in particular on how inflammation in the lung may be a nidus for both RA onset and worsened clinical outcomes.
The objective of this FAES Graduate School is to provide a deeper understanding of epidemiologic research methodology that can be used to interpret critically the results of epidemiologic research. This understanding is the result of investigating conceptual models for study designs, disease frequency, measures of association and impact, imprecision, bias, and effect modification. The course emphasizes the interpretation of research, even when the design or execution of the respective research is less than ideal.
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.
Genomics and Health Disparities Lecture Series: Exploring the Role of Genomics in Achieving Health Equality
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.
Geospatial Data for Healthy Places: Building Environments for Active Living Through Opportunistic GIScience
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.
In his Methods: Mind the Gap webinar, Dr. David Grossman focuses on the evidence gaps in children’s clinical preventive services and addresses how these gaps might be filled through a combination of different study designs that best address these gaps, including screening trials, treatment trials, and observational evidence across a broad variety of conditions.
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.
This webinar explores the topic of community and stakeholder engagement, partnership, and issues of measurement. Drs. Nina Wallerstein and Bonnie Duran provide an overview of their research in community-based participatory research (CBPR), in relevance to implementation science, and the measures they used to assess engagement and CBPR in action.
During this webinar, Drs. Proctor and Brownson discuss characteristics of high-impact implementation science as well as efforts to build capacity of the field through D&I research training. They present their take on the potential of the field, current limitations, and how efforts to build capacity can lead to the next set of advances.
In his webinar, Dr. Powell describes the development and refinement of a compilation of implementation strategies, emphasizes the importance of carefully specifying and reporting implementation strategies to ensure replicability, and discusses ongoing work focusing on the development of more effective ways of tailoring implementation strategies to specific contexts.
Implementation Science and Modelling Strategies: Experiences from NCI’s Cancer Intervention and Surveillance Modeling Network (CISNET)
Dr. David Chambers is joined by Dr. Eric ‘Rocky’ Feuer, Dr. Amy Trentham-Dietz, and Dr. Chin Hur for a brief overview of the CISNET consortium, and Drs. Trentham-Dietz and Hur will present case examples of how their cancer site modeling work addresses a range of implementation science topics, including de-implementation.