March 14, 2024: IMPACT Collaboratory Announces New Statistical Tools for Pragmatic Trials

The NIA IMPACT Collaboratory, a program to advance pragmatic clinical trials of interventions for people living with dementia and their care partners, announced a new collection of statistical tools for researchers. The tools are available on a new Statistical Tools webpage that will be updated as new resources become available.

The program’s Design and Statistics Core developed the statistical tools and related resources to aid in the design and analyses of pragmatic trials embedded in healthcare systems. These methods, manuscripts, statistical programs, and interactive web applications are available to help researchers calculate sample sizes, intracluster correlations, and statistical power for stepped-wedge and other cluster randomized designs.

The tools and other resources include:

  • Tool to calculate intracluster correlation coefficients for designing cluster randomized trials
  • Tool to simulate intracluster correlation coefficients among Medicare beneficiaries with dementia for hospitalizations, emergency department visits, and deaths across US hospital referral areas
  • Power analyses for stepped-wedge designs with multivariate continuous outcomes
  • Power and sample size requirements for generalized estimating equation analyses of cluster randomized crossover trials
  • Information content of stepped-wedge designs when treatment effect heterogeneity and/or implementation periods are present

More than 5 million Americans are living with Alzheimer disease and related dementias. They are particularly vulnerable to receiving uncoordinated and poor-quality care, which contributes to adverse health outcomes and misuse of resources. The mission of the IMPACT Collaboratory is to advance care for persons with dementia and their caregivers in real-world settings by building national capacity to conduct pragmatic clinical trials that test interventions embedded in healthcare systems.

Visit the IMPACT Collaboratory’s Statistical Tools web page.

The IMPACT Collaboratory is supported by a grant from the National Institute on Aging.

April 14, 2022: FDA Announces New Draft Guidance for Increasing Enrollment of Diverse Populations in Clinical Trials

FDA logoThe US Food and Drug Administration (FDA) issued draft guidance yesterday recommending clinical trial sponsors develop a “Race and Ethnicity Diversity Plan” to ensure representative enrollment of racially and ethnically diverse participants in clinical trials developing medical products.

The draft guidance, Diversity Plans to Improve Enrollment of Participants From Underrepresented Racial and Ethnic Populations in Clinical Trials Guidance for Industry, updates previous FDA guidance issued in October 2016. The “Race and Ethnicity Diversity Plan” is recommended for studies submitting IDE or IND applications to the FDA for approval of an investigational drug or device. The updated guidance provides information about 5 elements that should be included in the plan:

  • Overview of the disease/condition
  • Scope of medical product development program
  • Goals for enrollment of underrepresented racial and ethnic participants
  • Specific plan of action to enroll and retain diverse participants
  • Status of meeting enrollment goals

Achieving heath equity for underrepresented racial and ethnic populations starts with appropriate representation in clinical trials. Disease burden is often higher for underrepresented populations, yet barriers to participation in clinical trials may prevent adequate enrollment. Improving racial and ethnic diversity in clinical trials ensures that results are generalizable and medical discoveries are safe and effective for all patients.

The draft guidance was a collaborative effort between the Oncology Center of Excellence’s Project Equity, the Center for Drug Evaluation and Research, the Center for Biologics Evaluation and Research, and the Center for Devices and Radiological Health.

Read the FDA news release.

February 11, 2022: Great Power and Great Responsibility: Machine Learning in Clinical Research (E. Hope Weissler, MD, MHS; Erich Huang, MD, PhD)

Speakers

E. Hope Weissler, MD, MHS
Resident, Vascular and Endovascular Surgery
Duke University School of Medicine

Erich Huang, MD, PhD
Chief Science and Innovation Officer, Onduo

Topic

Great Power and Great Responsibility: Machine Learning in Clinical Research

Keywords

Machine Learning; Artificial Intelligence; Data Liquidity; Data Storage; HL7FHIR

Key Points

  • Machine learning may address issues that have reduced the efficiency and effectiveness of clinical research and help clinical research projects reach their full potential.
  • Machine learning may improve the pragmatism of research, decreasing costs and time it takes to conduct a research study.
  • Machine learning can be used to canvas the literature, hypothesize drug-target interactions, propose new therapeutics, and analyze highly dimensional research output.
  • Effects of machine learning are up to us and could potentially reduce the pragmatism of research if applied indiscriminately. Machine learning could produce overly selected study participant groups, too closely managing adherence, and using ultra-high-touch follow-up methods.
  • Data Liquidity refers to the ease with which data can be transferred or exchanged. This depends largely on the manner in which the data is stored.
  • Some forms of data are liquid than others due to privacy, security, and ethical concerns.

Discussion Themes

A lot of emphasis is currently being placed on the mobile/wearable device area, but an equally important area to develop in machine learning is patient identification and recruitment.

Is data ever really de-identified? Should data be owned by the patient? Why is health data treated differently than consumer data? Privacy regulation is difficult and needs to be addressed further by Congress in the future.

 

Read more about Dr. Weissler and Dr. Huang’s machine learning in clinical research.

 

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May 7, 2021: Online Recruitment in the Era of COVID-19: Pitfalls and Progress (Megan L. Ranney, MD, MPH)

Speaker

Megan L. Ranney, MD, MPH, FACEP
Director, Brown-Lifespan Center for Digital Health
Warren Alpert Endowed Associate Professor of Emergency Medicine, Brown University
Associate Dean of Strategy and Innovation, School of Public Health, Brown University
Chief Research Officer, AFFIRM
Co-founder, GetUsPPE

Topic

Online Recruitment in the Era of COVID-19: Pitfalls and Progress

Keywords

COVID-19; Online trial recruitment; Clinical trials; Emergency medicine; Digital health technologies; Remote interventions; Electronic informed consent

Key Points

  • The Center for Digital Health at Brown University is a research and education hub that explores innovative solutions to urgent health challenges. The Center has supported studies involving the use of digital health technologies for recruiting participants and delivering behavioral health interventions.
  • Due to disruptions caused by the COVID-19 pandemic, many research studies pivoted from in-person contact toward the use of digital technologies such as smartphone apps and remote telehealth.
  • To advance clinical trials in a post-pandemic world, we will need to establish best practices for digital health technologies—and recognize when online recruitment is appropriate and when it is not. Hybrid recruitment models offer a solution.

Discussion Themes

It remains clear that the relationship between study staff and participants is essential to forming positive alliances and determines the likelihood of follow up.

For social media advertising, it’s possible that an IRB could approve a group of images, headlines, and content that study teams can combine in different ways to optimize the advertising over the course of a study.

The Pew Research Center provides recent data on which social media platforms are used most by Americans. Read more about digital health science at the Center for Digital Health.

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January 22, 2021: Is It Time to Embrace Preprints? A Conversation About the First 18 Months of medRxiv (Harlan Krumholz, MD, SM; Joseph Ross, MD, MHS)

Speakers

Harlan M. Krumholz, MD, SM
Harold H. Hines, Jr. Professor of Medicine and Public Health
Yale University

Joseph S. Ross, MD, MHS
Professor of Medicine and Public Health
Yale University

Topic

Is It Time to Embrace Preprints? A Conversation About the First 18 Months of medRxiv

Keywords

Preprints; Preprint server; medRxiv; Open science; Health science research; Research transparency; Preliminary research reports

Key Points

  • A preprint is a research manuscript yet to be certified by peer review and accepted for publication by a journal. A preprint server, like medRxiv, is an online platform dedicated to the distribution of preprints.

  • MedRxiv is publisher-neutral. It is operated by the Cold Spring Harbor Laboratory and managed in partnership with BMJ and Yale University.

  • Server submission requirements for authors help to mitigate concerns about preprints. These include clear posting criteria (ie, original research articles only), an established screening process, and a caution to users of preprints, including researchers, journalists, and the public, that states: “Preprints are preliminary reports of work that have not been peer-reviewed. They should not be relied on to guide clinical practice or health-related behaviors and should not be reported in news media as established information.”

  • Not allowed are commentaries, editorials, opinion pieces or essays, letters to editors, narrative reviews, medical-legal research, and case reports.

Discussion Themes

The concept of “living data” or “living analyses” has grown out of the pandemic crisis and could stay on as a feature of scientific communication.

How do you think the public conversation around a preprint may positively or negatively impact the peer review process itself?

How are academic institutions acknowledging preprints in the sense of “evidence of productivity” (as for academic promotion)?

Preprints can serve as a teaching opportunity not only for reminding scientists to be discerning readers of reported science, but also for reminding the media.

Learn more about medRxiv.

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January 26, 2021: NIH Collaboratory COVID-19 Grand Rounds Continues With the COVID-19 Citizen Science Study

Dr. Gregory MarcusIn this week’s COVID-19 Grand Rounds session, Dr. Gregory Marcus of the University of California, San Francisco, will present “The COVID-19 Citizen Science Study.” The Grand Rounds session will be held on Friday, January 29, at 1:00 pm eastern. Join the online meeting.

The Citizen Science Study is using a smartphone-based research platform to engage “citizen scientists” in advancing understanding of COVID-19. By collecting information from tens of thousands of participants, researchers hope to gain insights into how the coronavirus is spreading, identify ways to reduce the number of new infections, and determine how COVID-19 is affecting individuals and populations.

The NIH Collaboratory Coordinating Center is using its popular Grand Rounds platform to share late-breaking research and promote resources in support of clinical researchers affected by the COVID-19 public health emergency.

For previous COVID-19 Grand Rounds, and more news and resources related to the COVID-19 public health emergency, see the COVID-19 Resources page.

September 25, 2020: Accelerating the Nation’s COVID-19 Testing Capacity: An Update from the NIH’s RADx Program (Rick Bright, PhD; Rachael Fleurence, PhD)

Speakers

Rick Bright, PhD
Senior Advisor to the NIH Director

Rachael Fleurence, PhD
Special Assistant to the NIH Director for COVID-19 Diagnostics

Topic

Accelerating the Nation’s COVID-19 Testing Capacity: An Update from the NIH’s RADx Program

Keywords

National Institutes of Health; COVID-19; Rapid Acceleration of Diagnostics (RADx); COVID-19 testing protocols; Innovative technologies; Coronavirus testing

Key Points

  • The NIH launched the Rapid Acceleration of Diagnostics (RADx) initiative to speed innovation in the development, commercialization, and implementation of technologies for COVID-19 testing.
  • RADx is creating programs to rapidly scale-up testing across the country and enhance access to those most in need. Newer technologies offer user-friendly designs with lower cost and increased accessibility at home and at the point of care.
  • Deploying the right tests at the right time to the right people will be critical to managing the pandemic until a vaccine is available and beyond. Testing will still be necessary after the vaccine becomes available.

Discussion Themes

The supply chain continues to be a challenge in COVID-19 testing procedures, for example the availability of plastic tips and swabs. However, barriers are driving innovations such as saliva technologies and extraction-less approaches.

Are there efforts underway to link testing data from disparate sources such as EHR clinical data, administrative claims data, antibody testing, symptom trackers/COVID-19 registries?

A new goal will be implementing the real-time matching of COVID-19 hot spots with available testing.

Read more about the NIH’s RADx program and in a special report in New England Journal of Medicine.

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August 7, 2020: The Democratization of Medicine: Open Access, Social Media, AI, Apps, and Empowering the Patient as the Future of Clinical Research (C. Michael Gibson, MS, MD)

Speaker

C. Michael Gibson, MS, MD
Professor of Medicine
Harvard Medical School
President and CEO
Baim and PERFUSE Research Institutes

Topic

The Democratization of Medicine: Open Access, Social Media, AI, Apps, and Empowering the Patient as the Future of Clinical Research

Keywords

Clinical research; Open access; Social media; Artificial intelligence; Heartline study; WikiDoc; WikiPatient

Key Points

  • As the internet is replacing the printing press, “copyleft” is replacing copyright in the open-access era. It is a participatory community with bidirectional flow of information through social media.
  • Health data does not equal health care. Patients are looking to physicians to curate health information from huge volumes of data.
  • Social media and open access during the COVID-19 pandemic has meant that physicians are citizen journalists, innovators, activists, and educators.
  • In this new world, patients are enrolling in virtual trials via a phone app and will be followed up online through claims data and patient-reported outcomes.

Discussion Themes

The COVID-19 pandemic has been a call to arms to clinicians to combat not only the virus but the misinformation. As educators we must set the path and not allow uninformed people to take control.

Enabling patient-empowered trials has the potential for more generalizable study results and can lead to patient-specific predictions through use of artificial intelligence.

How do we validate the quality of open-access data and reports that are not peer-reviewed?

How can we diminish the hazards of skewed research outcomes arising from trial participant conversations on social media?

Read more from C. Michael Gibson in The Democratization of Medical Research and Education Through Social Media: The Potential and the Peril (JAMA Cardiology 2017).

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July 14, 2020: Grand Rounds Webinar Presents the New N3C Analytics Platform for COVID-19 Research

Watch the recent Grand Rounds webinar presented by Dr. Ken Gersing of the National Center for Advancing Translational Sciences and Dr. Robert Star of the National Institute of Diabetes and Digestive and Kidney Diseases to learn more about the COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C).

The N3C initiative aims to build a centralized national data resource that researchers can use to study COVID-19 and identify potential treatments as the pandemic continues to evolve. N3C is a partnership among the Clinical and Translational Science Awards Program hubs and the National Center for Data to Health, with overall stewardship by the National Center for Advancing Translational Sciences (NCATS).

The goals of N3C are to:

  • Rapidly collect and aggregate clinical, lab, and imaging data from hospitals, health plans, and CMS at the peak of the COVID-19 pandemic and as it evolves
  • Provide a longitudinal dataset to understand acute hospital and recovery phases
  • Understand pathophysiology of disease
  • Support clinical trials by identifying patients who might wish to participate in trials

Watch the Grand Rounds webinar or download the slides. For more details, visit the NCATS N3C website.

July 10, 2020: COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C) (Ken Gersing, MD; Robert Star, MD)

Speakers

Ken Gersing, MD
Director of Informatics, NCATS
National Institutes of Health  

Robert A. Star, MD
Director, Division of Kidney, Urologic, and Hematologic Disorders, NIDDK
Chief, Renal Diagnostics and Therapeutics Unit, NIDDK
National Institutes of Health  

Topic

COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C)

Keywords

COVID-19; Coronavirus; Pandemic; Data exchange; Data use agreement; Phenotypes; Data harmonization; Common data model; Fast Healthcare Interoperability Resources (FHIR); Synthetic data

Key Points

  • The National COVID Cohort Collaborative (N3C) initiative aims to build a centralized national data resource that the research community can use to study COVID-19 and identify potential treatments as the pandemic continues to evolve.

  • N3C is a partnership among the Clinical and Translational Science Awards Program hubs and the National Center for Data to Health, with overall stewardship by the National Center for Advancing Translational Sciences (NCATS).

  • The goals of N3C are to:
    • Rapidly collect and aggregate clinical, lab, and imaging data from hospitals, health plans, and CMS at the peak of the COVID-19 pandemic and as it evolves
    • Provide a longitudinal dataset to understand acute hospital and recovery phases
    • Understand pathophysiology of disease
    • Support clinical trials by identifying patients who might wish to participate in trials

Discussion Themes

The N3C analytics platform is cloud-based and provides a secure data enclave. Data can be received via multiple data models and transformed into a common analytic model for research.

As a centralized data model, N3C complements existing federated data models like PCORnet and OMOP. The tool does not replace the need for randomized controlled trials.

NCATS, FDA, and NCI are working together on common data model (CDM) harmonization so that data will be publicly available and reusable in human and machine-readable formats.

Read more on the NCATS N3C website as well as view a short video demonstration.

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