In the latest episode of the NIH Collaboratory Grand Rounds podcast, Dr. John Concato and Dr. Richard Platt continue their discussion about the FDA draft guidance on real-world evidence.
This podcast continues the discussion with Dr. John Concato as he discusses the FDA draft guidance on real-word evidence. Click on the recording below to listen to the podcast.
John Concato, MD, MS, MPH
Associate Director for Real-World Evidence Analytics
Office of Medical Policy (OMP)
Center for Drug Evaluation and Research (CDER)
Food and Drug Administration (FDA)
Keywords
Big data; Real-word evidence; Real-world data; 21st Century Cures Act; FDA Draft Guidance
Key Points
Big Data, a term first used in the 1990s, leverages modern technology to increase the quantity, forms, speed, and capability to manipulate large-scale data. Real-world data (RWD) is a term with specific regulatory implications referring to health care data routinely collected from a variety of sources. Real-world evidence (RWE) is clinical evidence derived from analysis of RWD regardless of study design.
Terminology is important in research work, and we should strive to be as precise as possible with the terminology we use.
With the 21st Century Cures Act of 2016, the FDA established a program to evaluate the potential use of real-world evidence to support new indications for drugs and satisfy post-approval study requirements.
In 2021, the FDA issued 4 draft guidance documents for Real-world data and Real-world evidence intended to guide the selection and management of data sources to appropriately address the study question and support decision-making for drug and biological products.
Discussion Themes
– Could real-world data sources be certified and preclude the need for submission of source data on a study specific basis? From the FDA point-of-view, while reliability can be more readily evaluated and would tend to be more stable, the relevance to a particular study could not be determined as easily.
– While there can be a reflex that says we can never be sure about major confounding, it should not be the miasma of the 21st century. A thoughtful approach that considers the characteristics that matter is the best approach.
AcademyHealth is accepting abstracts for the 14th Annual Conference on the Science of Dissemination and Implementation in Health until July 27, 2021. This year’s meeting will be held virtually from December 14 to 16.
The theme of this year’s virtual online meeting is “Broadening Horizons for Impact: Incorporating Multisectoral Approaches into D&I Science.” The annual conference is cohosted by the NIH and AcademyHealth with the goal of realizing “the full potential of evidence to optimize health and health care by bridging the gap between research, practice, and policy.”
This webinar will explore perspectives on the challenges and opportunities in accessing controlled data stewarded by the NIH. The event will include opportunities to hear from experts on the topic and to ask questions and provide ideas with follow-up activities. The webinar will be of particular interest to data scientists and investigators who use NIH data resources.
Organized by the NIH Controlled Data Access Coordination Working Group, the webinar will help inform the group’s recommendations to NIH leadership on ways to streamline access to controlled data.
The Grand Rounds session will be held on Friday, October 2, at 1:00 pm eastern. Join the online meeting.
Since 2010, PCORI has funded more than $2 billion in research to help patients, caregivers, and clinicians make informed healthcare decisions and to improve healthcare delivery and outcomes.
Abhinav Sharma, MD, PhD
Assistant Professor of Medicine
McGill University
Christopher B. Granger, MD, FAHA, FACC
Professor of Medicine
Director, Cardiac Intensive Care Unit
Duke University Medical Center
Topic
Impact of Regulatory Guidance on Evaluating Cardiovascular Risk of New Glucose-Lowering Therapies to Treat Type 2 Diabetes Mellitus–Lessons Learned and Future Directions
Keywords
Type 2 diabetes; Regulatory; Cardiovascular risk; Food and Drug Administration; FDA; Patient outcomes; Anti-hyperglycemic medications
Key Points
The hallmark of type 2 diabetes mellitus is insulin resistance and relative insulin deficiency. Ninety percent of all cases of diabetes are type 2 diabetes, and the diagnosis can occur at any age.
While people with type 2 diabetes can often initially manage their condition through exercise and diet, over time most people will require oral drugs or insulin.
Strategies are needed reduce the burden of cardiovascular outcomes in patients with diabetes.
Is the cardiovascular protection of some anti-hyperglycemic drugs independent of the effect on blood glucose?
How can regulators, industry, academia, payers, and patient advocacy groups assure that evidence generation to improve care is incentivized without undue regulatory burdens?
Should post-marketing studies include comparative effectiveness pragmatic trials in order to improve translation into clinical practice?
Rebecca Li, PhD
Executive Director, Vivli
Co-Director of Research Ethics, Harvard Center for Bioethics
Harvard Medical School
Frank W. Rockhold, PhD
Professor of Biostatistics and Bioinformatics
Duke Clinical Research Institute
Duke University Medical Center
Topic
Preparing for Clinical Trial Data Sharing and Re-use: The New Reality for Researchers
Keywords
Data sharing; Individual patient data; Open access; Raw data; ICMJE; Research dissemination
Key Points
Open access to individual patient data from clinical trials is a critical tool for research in health care. Despite the challenges, the question is not whether data should be shared, but rather how and when access should be granted.
Preparing data for reuse is often an afterthought—yet it is a new reality for researchers and institutions.
As of January 1, 2019, the International Committee of Medical Journal Editors (ICMJE) requires registration of a trial’s data sharing plan at the time of trial registration.
Institutions or teams should begin their data sharing program planning at least 18 months before a major publication (or regulatory approval).
Discussion Themes
FAIR data are data that meet standards of findability, accessibility, interoperability, and reusability.
How do we manage scientific integrity, replication, and validity given that data sharing opens a study to multiple people asking the same or related questions in potentially different ways using different methods?
How do we plan for a future that rewards data quality and reuse?
The Food and Drug Administration (FDA) is proposing a rule to allow for a waiver or alteration of informed consent for clinical investigations posing no more than minimal risk to human participants. This rule would align FDA regulations with the Common Rule, reduce burden and costs for Institutional Review Boards, and be expected to lead to advances in healthcare.
“We expect benefits in the form of healthcare advances from minimal risk clinical investigations and from harmonization of FDA’s informed consent regulations with the Common Rule’s provision for waiver of informed consent for certain minimal risk research.” — Federal Register /Vol. 83, No. 221
Currently, FDA allows a waiver or alteration of consent only in life-threatening situations. If aligned with the Common Rule, a waiver or alteration would be allowed if the IRB finds and documents that 1) the research involves no more than minimal risk, 2) the rights and welfare of subjects will not be adversely affected, 3) the research could not practicably be carried out without a waiver, and 4) the participants will be provided with additional pertinent information after completion of the trial.
On June 4, the National Institutes of Health (NIH) released its first Strategic Plan for Data Science. The plan outlines steps the agency will take to modernize research data infrastructure and resources and to maximize the value of data generated by NIH-supported research.
Data science challenges for NIH have evolved and grown rapidly since the launch of the Big Data to Knowledge (BD2K) program in 2014. The most pressing challenges include the growing costs of data management, limited interconnectivity and interoperability among data resources, and a lack of generalizable tools to transform, analyze, and otherwise support the usability of data for researchers, institutions, industry, and the public.
The goals of the NIH Strategic Plan for Data Science are to:
support an efficient, effective data infrastructure by optimizing data storage, security, and interoperability;
modernize data resources by improving data repositories, supporting storage and sharing of individual data sets, and integrating clinical and observational data;
develop and disseminate both generalizable and specialized tools for data management, analytics, and visualization;
enhance workforce development for data science by expanding NIH’s internal data science workforce and supporting expansion of the national research workforce, and by engaging a broader community of experts and the general public in developing best practices; and
enact policies that promote stewardship and sustainability of data science resources.
As part of the implementation of the strategic plan, the NIH will hire a chief data strategist.