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.
Improving Measures of Physical Activity, Sedentary Behavior, and Context for Epidemiological Studies and Interventions in Older Adults
Dr. Kerr is a leader in physical activity research and assessment in older adults. Her webinar outlines the importance of improving measurement precision of both physical activity and sitting time using mobile sensors and machine learning techniques. Dr. Kerr discusses the infrastructure investments that have been necessary, challenges of working with computer scientists, and the need for stronger validation of new measurement techniques.
Improving the Efficiency of Prevention Research Using Responsive and Adaptive Survey Design Techniques
In this Methods: Mind the Gap presentation, Dr. Wagner starts from a definition of the basic principles of responsive and adaptive designs and then provides concrete examples of the implementation of these designs. These examples are drawn from a variety of settings, including face-to-face, telephone, and mixed-mode surveys.
Dr. Ioannidis is the leading researcher worldwide on meta-research, the systematic evaluation of research practices and how they can be optimized.
In his Methods: Mind the Gap presentation, Dr. Robert Califf discusses the role and value of clinical trials in medical research given the rapid evolution of the science of clinical trials.
This innovative program will place a strong emphasis on mentoring, applying competencies and curriculum specifically focused on chronic disease disparities, and working with a diverse set of partners. Scholars are enrolled in the program for two years.
The objective of this FAES Graduate School course is to provide an introduction to the principles and methods of epidemiology, defined as the study of the distribution and determinants of disease in populations. Lectures, problem sets, and outside reading will cover ecologic, case-control, cohort, and experimental studies. Topics to be discussed will include study design, measures of disease risk, sources of bias, methods of controlling for extraneous factors, principles of screening, and interpretation of data. Illustrations will include classic and contemporary examples in acute and chronic disease.
This FAES course covers the basics of some tools that students can subsequently use to work with Data Science, such as Hadoop’s MapReduce, Apache Spark, Pig, Hive, Python, and R. In addition, the course will cover advanced data structures as well as real-world data scraping, cleansing, and wrangling. The course will also include a high-level overview of machine-learning concepts.
Python is a free, open-source and powerful programming language that is easy to learn. This FAES course is intended for nonprogrammers who want to learn how to write programs that expand the breadth and depth of their daily research. Most elementary concepts in modern software engineering are covered, including basic syntax, reading from and writing texts files, debugging python programs, regular expressions, and creating reusable code modules that are distributable to peers. The course also focuses on potential applications of Python to bioinformatics, including sequence analysis, data visualization, and data analysis. Students also learn to use the Jupyter Notebook and the PyCharm integrated development environment (IDE), which are available at no cost.
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.
The course covers the fundamentals of the SAS program and its variables, creating data, importing data (from text and Excel files), exporting data (to text, pdf, and Microsoft-related formats), manipulating data, and providing descriptive statistics. Students have the opportunity to practice in class, using sample datasets. Homework and project assignments will be provided as well.
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.
In this Methods: Mind the Gap webinar, Dr. Max Crowley discusses key standards for economic evaluation as identified by a number of convergent efforts. In particular, the important role of administrative records for mapping the costs and benefits of prevention onto public budgets are discussed. Participants will gain a greater awareness of (1) best practices for economic evaluations of prevention, (2) how to increase utility of estimates for budget making, and (3) opportunities to include economic evaluation in ongoing and new studies.
In this Methods: Mind the Gap presentation, Dr. Tucker discusses the approaches to dietary assessment for estimating usual intake for the purpose of relating intake of nutrients, foods, and food patterns to chronic health conditions such as diabetes, heart disease, stroke, cognitive decline, bone loss, and others.
UMass Lowell, Zuckerberg College of Health Sciences
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.
Joint Models of Longitudinal and Time-to-Event Data for Informing Multi-Stage Decision Making in mHealth
In this Methods: Mind the Gap presentation, Dr. Dempsey focuses on mHealth studies in which both longitudinal and time-to-event data are recorded per participant. From assessing levels of biomarker association with event risk, to defining risk strata for a stratified micro-randomized trial, to post-study analysis of the treatment effect on event risk, he discusses how joint models enter into various stages of the intervention development process. He also discusses how mHealth studies present novel methodological challenges for joint modeling and solutions in several case studies. In each instance, he connects the joint modeling perspective back to how scientists can use them to inform multi-stage decision making in mHealth.