Dr. Geoffrey Curran’s presentation addresses the topic of hybrid effectiveness-implementation studies, a set of approaches to simultaneously studying the effectiveness of health interventions and the strategies to implement them in community and clinical practice settings. His presentation unpacks the rationale for these designs, a typology of designs based on the state of science for a given intervention, and provides examples of studies utilizing these important methods.
Part of a joint presentation, Dr. Rachel Tabak presents a review which uses snowball sampling to develop an inventory of models, synthesizes this information, and provide guidance on how to select a model. Dr. Ted Albert Skolarus discusses an examination of citation frequency and impact of D&I models using citation analysis.
In this Methods: Mind the Gap webinar, Dr. Jason Moore reviews the new discipline of automated machine learning (AutoML). The goal of AutoML is to simplify the process of combining different types of algorithms and methods in an analytical pipeline and to make machine learning more accessible.
In this webinar, Drs. Larry Green and Rachel Gold deliver a joint presentation on fidelity and adaptation. Fidelity and adaptation relate to the manner in which the evidence from a research study is brought to practice. There is fidelity if the program is implemented in a way that is very similar to how it was originally designed, and there is adaptation when there are changes made to the process and content of the program to fit to a particular context. In most cases, contextual factors can influence the ability to maintain fidelity as well as the need for adaptation.
A collection of online chapters that provide an introduction to selected behavioral and social science research approaches, including theory development and testing, survey methods, measurement, and study design. eSource was developed in 2010, and these chapters have not been updated to reflect advances in the past decade. However, they can still be used as supplementary teaching materials.
A report that provides guidance to NIH investigators on how to rigorously develop and evaluate mixed methods research applications.
Big Data and the Promise and Pitfalls When Applied to Disease Prevention and Promoting Better Health
How disruptive will Big Data be in the long run to biomedical research and health care? In his Methods: Mind the Gap webinar, Dr. Philip Bourne addresses this question in light of the Big Data to Knowledge (BD2K) initiative and other trans-NIH data science programs.
This archive provides a collection of webinars on methodology. The topics include HIV prevention, implementation methods, personalized medicine, complexity, and longitudinal data. In 2017, the Office of Disease Prevention (ODP) provided co-funding to the Center for Prevention Implementation Methodology to help create this archive.
A collection of training modules that came out of the NIH's initiative to enhance rigor and reproducibility in the research endeavor. The modules were developed by the NIH or NIH-funded grantees and focus on a variety of topics, including integrating sex and gender into research, the design and analysis of group-randomized trials, and computational analyses.
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, 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.
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 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.
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.
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 is the first part of a two-part course. Registration is required separately for each part of the course.
This week-long immersion program provides 30 selected investigators with a thorough introduction to selected mHealth methodologies that may be used to study behavioral and social dimensions of public health. Participants work with expert mentors to create their own inter-disciplinary mobile health projects.
The mHealth training institute is funded via the NIH BD2K Program. The NIH BD2K Program is funded by all the NIH Institutes and Centers and receives support from the NIH Common Fund and the NIH Office of Behavioral Health and Social Sciences Research (OBSSR).
Mixing qualitative and quantitative research methods can provide deeper exploration of causal mechanisms, interpretation of variables, and contextual factors that may mediate or moderate the topic of study. In this Methods: Mind the Gap webinar, Dr. Leonard Jason provides an introduction to the different approaches used in conducting mixed-methods research, including the benefits and challenges.
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.
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.