Evidence map›Paper›PMID 41963997›Full record

ArticleTrials2026

The power, potential of real-world data in randomized controlled trials: proceedings from a multistakeholder think tank.

Nina Nouhravesh, Jennifer G Jackman, Adrian F Hernandez, Charles Lee, Christoph P Hornik, Emily Zacherle, Joanne Waldstreicher, Noelle Cocoros, Samuel Brown, Tor Biering-Sorensen and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Nina NouhraveshDuke Clinical Research Institute, Duke University School of Medicine, Durham Center, DUMC #3850, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA.
Jennifer G JackmanDuke Clinical Research Institute, Duke University School of Medicine, Durham Center, DUMC #3850, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA.
Adrian F HernandezDuke Clinical Research Institute, Duke University School of Medicine, Durham Center, DUMC #3850, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA.
Charles LeeAstraZeneca, Gaithersburg, MD, USA.
Christoph P HornikDuke Clinical Research Institute, Duke University School of Medicine, Durham Center, DUMC #3850, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA.
Emily ZacherleNovo Nordisk, Pittsburgh, PA, USA.
Joanne WaldstreicherIndependent Board Member and Consultant, Westfield, NJ, USA.
Noelle CocorosHarvard Pilgrim Health Care Institute, Boston, MA, USA.
Samuel BrownIntermountain Health, Salt Lake City, UT, USA.
Tor Biering-SorensenDepartment of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
Karen ChiswellDuke Clinical Research Institute, Duke University School of Medicine, Durham Center, DUMC #3850, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA.
Lisa WruckDuke Clinical Research Institute, Duke University School of Medicine, Durham Center, DUMC #3850, 300 West Morgan Street, Suite 800, Durham, NC, 27701, USA. lisa.wruck@duke.edu.
Stefan K JamesUppsala Clinical Research Center, Uppsala University, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Randomized controlled trials (RCTs) remain the gold standard for evaluating medical interventions, but they often face challenges related to patient recruitment, cost, and efficiency. Real-world data (RWD) has emerged as a valuable tool to enhance trial design, improve patient identification, and support regulatory decision-making. However, integrating RWD into RCTs presents methodological, regulatory, and operational challenges. To address these issues, a think tank was convened in May 2024 at the Duke Clinical Research Institute, bringing together experts from academia, industry, healthcare systems, regulatory agencies, and patient advocacy groups. Discussions focused on three key areas: optimizing patient identification and outcome assessment, leveraging RWD for safety assessments, and using RWD in RCTs supporting regulatory approval. RWD has the potential to simplify eligibility criteria, enhance recruitment through artificial intelligence, and provide practical endpoints for evaluating treatment effects. The think tank underscored the need for collaboration across stakeholders to address challenges, such as data inconsistencies, privacy concerns, and infrastructure limitations. The event concluded with actionable recommendations, including the following: (1) standardizing RWD sources to ensure consistency and improve interoperability across healthcare systems, (2) developing regulatory frameworks that define acceptable use cases for RWD in clinical trials, (3) enhancing data quality through robust validation methodologies and real-time monitoring, (4) investing in artificial intelligence-driven patient identification tools to streamline recruitment, and (5) fostering multi-stakeholder collaboration to align expectations and share best practices. Moving forward, implementing these strategies will be critical to fully harness the potential of RWD in clinical research and improve trial efficiency.

Indexed as

Randomized Controlled Trials as TopicResearch DesignHumansPatient SelectionStakeholder ParticipationElectronic health recordsEndpointsPatient recruitmentRandomized controlled trialsReal-world dataRegistries

Identifiers

PMID41963997
PMCPMC13181875

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.