research methods

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)

Posted on by Gina Uhlenbrauck

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.

Tags

#pctGR, @Collaboratory1

August 28, 2020: Designing, Conducting, Monitoring, and Analyzing Data from Pragmatic Clinical Trials: Proceedings from a Multi-Stakeholder Think Tank Meeting (Trevor Lentz, PT, PhD, MHA; Lesley Curtis, PhD; Frank Rockhold, PhD)

Posted on by Gina Uhlenbrauck

Speakers

Trevor Lentz, PT, PhD, MHA
Assistant Professor in Orthopaedic Surgery
Duke Clinical Research Institute

Lesley Curtis, PhD
Chair and Professor, Department of Population Health Sciences
Duke University School of Medicine

Frank Rockhold, PhD, ScM, FASA, FSCT
Professor of Biostatistics and Bioinformatics
Duke Clinical Research Institute

Topic

Designing, Conducting, Monitoring, and Analyzing Data from Pragmatic Clinical Trials: Proceedings from a Multi-Stakeholder Think Tank Meeting

Keywords

Pragmatic clinical trials; Think tank; Risk-based monitoring; Data quality; Real-world data; Electronic health records

Key Points

  • Pragmatism in study design is not a binary concept: some trial elements are purely explanatory (to establish efficacy in ideal settings) and some elements are purely practical (to establish effectiveness in the real world). The study design must serve the research question.
  • Findings from the think tank discussions on best practices and actionable steps included:
    • Ask precise research questions, and select the appropriate degree of pragmatism.
    • Optimize data quality through study design.
    • Focus on primary endpoints in data capture to maximize likelihood of success.
    • Innovate on mechanisms for data capture.
    • Promote adherence to the study protocol.
    • Evolve trial operations staff to focus on data science and informatics.
    • Share learning experiences openly and widely.

Discussion Themes

There is a misconception that PCTs, because they pursue pragmatism, are less rigorous and conducted without proper oversight or adherence to a protocol. Quality by design and good clinical practice principles apply equally to PCTs.

Risk-based monitoring is a potentially dynamic system that could improve study safety and quality, and make better use of study resources.

There is great interest from regulators, sponsors, and the academic research community to move PCT methods forward. To achieve this, we need to see more examples of successful PCTs in a context of regulatory decision-making.

Read the proceedings from the think tank meeting published in Therapeutic Innovation & Regulatory Science.

Tags

#pctGR, @Collaboratory1

August 31, 2020: Newly Validated Sample Size Formula Detects Heterogeneity of Treatment Effect in Cluster Randomized Trials

Posted on by Damon Seils

Cover of Statistics in MedicineIn a study supported by the NIH Collaboratory, researchers developed and validated a new sample size formula for detecting heterogeneity of treatment effect in cluster randomized trials. The work was published this month in Statistics in Medicine.

Cluster randomization is frequently used in pragmatic clinical trials embedded in healthcare systems. Although cluster randomized trials are typically designed to evaluate the overall treatment effect in a study population, investigators are increasingly interested in studying differential treatment effects among subgroups.

The NIH Collaboratory investigators used extensive computer simulations to validate the new formula. They illustrate the procedure in a dataset from a large clinical trial.

In a previous study published last year, the same research team used computer simulation models validated by real-data simulations to reveal the influence of baseline covariate imbalance on treatment effect bias.

This work was supported within the NIH Collaboratory by the NIH Common Fund through a cooperative agreement from the Office of Strategic Coordination within the Office of the NIH Director, and by a research supplement from the NIH Common Fund to promote diversity in health-related research.

August 19, 2020: NIH Collaboratory COVID-19 Grand Rounds Series Puts Spotlight on Adaptive Platform Trials

Posted on by Damon Seils

Photo of Dr. Laura EssermanIn this week’s COVID-19 Grand Rounds session, Dr. Laura Esserman of the University of California, San Francisco will present “Adaptive Platform Trials: Scalable From Breast Cancer to COVID.” The Grand Rounds session will be held on Friday, August 21, at 1:00 pm eastern. Join the online meeting.

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.

Previous COVID-19 Grand Rounds:

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

July 31, 2020: Using Real-World Data to Plan Eligibility Criteria and Enhance Recruitment: Actionable Recommendations and Resources from the Clinical Trials Transformation Initiative (Sudha Raman, PhD, MA; John Sheehan, PhD, MBA, RPh)

Posted on by Gina Uhlenbrauck

Speakers

Sudha Raman, PhD, MA
Assistant Professor
Department of Population Health Sciences
Duke University

John Sheehan, PhD, MBA, RPh
Senior Director, Value and Evidence (HEOR) Neuroscience
Janssen Scientific Affairs, LLC

Topic

Using Real-World Data to Plan Eligibility Criteria and Enhance Recruitment: Actionable Recommendations and Resources from the Clinical Trials Transformation Initiative

Keywords

Clinical Trials Transformation Initiative (CTTI); Real-world data (RWD); Recruitment planning; EHR; Eligibility criteria; Fit-for-purpose data

Key Points

  • Real-world data (RWD) are data relating to patient health status and/or the delivery of health care routinely collected by a variety of sources.
  • CTTI provides recent recommendations, resources, and case studies that highlight actionable tools and best practices for evaluating and using real-world data (RWD) in clinical trial recruitment activities:
    • General principles for using RWD
    • Using RWD to plan eligibility criteria
    • Using RWD to support recruitment
    • Enhancing RWD capabilities for the research enterprise
  • Using RWD from data sources such as electronic health records and claims data brings challenges for completeness, accuracy, and generalizability of the data.
  • RWD holds the potential to increase patient eligibility and enrollment as well as reduce recruitment timelines.

Discussion Themes

Insights from RWD should be sought early in the product lifecycle and include context from patients and sites.

One challenge of RWD data sources is finding appropriate databases for the disease area of interest, especially for trials of rare diseases.

Are there lessons learned about when using RWD becomes prohibitive or too expensive?

Read and download CTTI’s recommendations for using RWD. Learn more about FDA’s guidance for real-world data and real-world evidence. A publication is available about the health plan recruitment method used in the ADAPTABLE aspirin study.

Tags

#pctGR, @Collaboratory1

July 17, 2020: Living Textbook Grand Rounds Series: Choosing What to Measure and Making it Happen: Your Keys to Pragmatic Trial Success (Devon Check, PhD; Rachel Richesson, PhD)

Posted on by Gina Uhlenbrauck

Speakers

Rachel Richesson, PhD, MPH
Associate Professor, Informatics
Duke University School of Nursing

Devon Check, PhD
Assistant Professor, Population Health Sciences
Department of Population Health

Topic

Choosing What to Measure and Making it Happen: Your Keys to Pragmatic Trial Success

Keywords

Measuring outcomes; Phenotypes; Data quality; Data linkage; Endpoints; Patient-reported outcomes (PROs)

Key Points

  • Endpoints and outcomes for embedded pragmatic clinical trials (ePCTs) should be meaningful to providers and patients and be relatively easy to collect as part of routine care. Endpoints and outcomes also should be clearly defined and reproducible.
  • Patient-reported outcomes (PROs) are often the best way to measure quality of life, but come with challenges in that they are not routinely or consistently used in clinical care nor are regularly recorded in the EHR.
  • To fully capture all care—complete longitudinal data—it is often necessary to link research and insurance claims data.

Discussion Themes

Data in EHRs are an important component of ePCTs. While ePCTs strive for efficiency, there remain tradeoffs. Sometimes it will be necessary to collect data outside of the EHR to ensure important and compelling results.

It is also important that the endpoint that is conveniently available will also be accepted as influential for stakeholders when the trial results are disseminated.

In the future, it is essential that more meaningful data as well as more patient-reported outcomes are routinely collected and incentivized.

Developing a robust data quality assessment plan will improve the value of data and detect and address data issues. Read more about how to do this in Assessing Data Quality for Healthcare Systems Data Used in Clinical Research.

To learn more about measuring outcomes, visit these Living Textbook chapters:

Tags

#pctGR, @Collaboratory1

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

Posted on by Gina Uhlenbrauck

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)

Posted on by Gina Uhlenbrauck

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.

Tags

#pctGR, @Collaboratory1, @ncats_nih_gov

June 26, 2020: Advances at the Intersection of Digital Health, Electronic Health Records, and Pragmatic Clinical Trials: Keys to Success in the Evolving EHR Environment (Keith Marsolo, PhD; Teresa Zayas-Cabán, PhD; George (Holt) Oliver, MD, PhD; Christopher A. Longhurst, MD, MS; Rachel Richesson, PhD, MPH)

Posted on by Gina Uhlenbrauck

Speakers

Guest Moderator:
Keith Marsolo, PhD
Associate Professor, Population Health Sciences
Duke University

Panel:
Teresa Zayas-Cabán, PhD
Chief Scientist
Office of the National Coordinator for Health Information Technology
Office of the Secretary, DHHS

George (Holt) Oliver, MD, PhD
Vice President Clinical Informatics
Parkland Center for Clinical Innovations

Christopher A. Longhurst, MD, MS
CIO and Associate CMO, Quality/Safety
Professor of Pediatrics and Medicine
UC San Diego Health

Rachel Richesson, PhD, MPH
Associate Professor
Duke University School of Nursing

Topic

Advances at the Intersection of Digital Health, Electronic Health Records, and Pragmatic Clinical Trials: Keys to Success in the Evolving EHR Environment

Keywords

Embedded PCTs; Electronic health records; EHR; Digital health; Data interoperability; Clinical decision support; Information technology

Key Points

  • A defining feature of the 19 NIH Collaboratory embedded pragmatic clinical trials is their use of the EHR, whether for eligibility screening, intervention delivery, and/or outcome assessment.
  • As an example, the ICD-Pieces NIH Collaboratory Trial showed that a standard set of EHR data can be used to identify patients. The study involved a diverse set of health systems, and the study team overcame many IT challenges, including integrating data from 3 different EHR systems.
  • It is possible to implement a system-wide data warehouse, as the University of California has done across its 5 academic medical centers.

Discussion Themes

The vision is to ensure that healthcare systems are able to learn from every patient, at every visit, every time.

A common challenge for trials embedded in healthcare delivery is access to operational IT expertise and the relative priority in those environments. How can we more effectively partner with our IT colleagues in these trials?

The U.S. Department of Health and Human Services released a comprehensive strategy to reduce the regulatory and administrative burden related to the use of health IT, including EHRs. Visit HealthIt.gov for more information.

Tags

#pctGR, @Collaboratory1

June 19, 2020: Living Textbook Grand Rounds Series: Part 4-Demystifying Biostatistical Concepts for Embedded Pragmatic Clinical Trials (Elizabeth Turner, PhD; Patrick Heagerty, PhD; David Murray, PhD)

Posted on by Gina Uhlenbrauck

Speakers

Elizabeth Turner, PhD
Associate Professor
Department of Biostatistics & Bioinformatics
Duke Global Health Institute
Duke University  

Patrick Heagerty, PhD
Professor Department of Biostatistics
University of Washington  

David Murray, PhD
Associate Director for Prevention
Director, Office of Disease Prevention National Institutes of Health

Topic

Demystifying Biostatistical Concepts for Embedded Pragmatic Clinical Trials

Keywords

Embedded PCTs; Biostatistics; Trial design; Cluster-randomized trial (CRT); Stepped-wedge; Intraclass correlation coefficient; NIH Collaboratory Trial; Sample size; Individually randomized group treatment

Key Points

  • Focus on the research question, because that will drive the design, and the design will drive the analysis.
  • Select design features with analysis in mind, and collaborate early with a statistician. Weigh statistical choices against the challenges of implementation.
  • If possible, choose individual randomization. However, sometimes there is a strong rationale for choosing cluster/group randomization. Clustering must be accounted for in both design and analysis for CRTs and individually randomized group treatment (IRGT) trials.
  • The intraclass correlation coefficient (ICC) is a common measure of outcome clustering. Estimating the ICC is needed for study planning and power.
  • Increasing the number of clusters has more impact on power than increasing the number of patients per cluster.

Discussion Themes

With the move to virtual healthcare, the boundaries between clinic-based clusters have become more fluid. What approaches should trials use to describe contamination and estimate the impact of contamination on outcomes?

Read more about ICC in a Living Textbook resource and visit the Training Resources page for practical help on how to plan and conduct ePCTs.

Learn more in the Living Textbook about considerations for trial design and analysis for ePCTs.

Visit the NIH Collaboratory’s Biostatistics and Study Design Core webpage for more resources around design and analysis issues in ePCTs.

The NIH hosts a Research Methods Resources website with materials on this topic.

Tags

#pctGR, @Collaboratory1