Evidence map›Paper›PMID 42834704›Full record

ReviewClinical pharmacology and therapeutics2026

Considerations for the Integration of Clinical Trials and Real-World Data.

Sky Qiu, Charles E Barr, Lauren Dang, Larry Han, Kajsa Kvist, Hana Lee, Andrew Mertens, Nerissa Nance, Lei Nie, Kara Rudolph and 10 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical pharmacology and therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Sky QiuDivision of Biostatistics, School of Public Health, University of California, Berkeley, California, USA.ORCID https://orcid.org/0009-0006-5944-0782
Charles E BarrAdaptic Health, Mountain View, California, USA.
Lauren DangNational Institute of Allergy and Infectious Diseases, Bethesda, Maryland, USA.
Larry HanDepartment of Public Health and Health Sciences, Northeastern University, Boston, Massachusetts, USA.
Kajsa KvistNovo Nordisk A/S, Bagsvaerd, Denmark.
Hana LeeCenter for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA.ORCID https://orcid.org/0000-0002-8684-1040
Andrew MertensCenter for Targeted Machine Learning and Causal Inference, School of Public Health, University of California, Berkeley, California, USA.
Nerissa NanceCenter for Targeted Machine Learning and Causal Inference, School of Public Health, University of California, Berkeley, California, USA.
Lei NieCenter for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA.
Kara RudolphDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York City, New York, USA.
Xu ShiDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Jens TarpNovo Nordisk A/S, Bagsvaerd, Denmark.
Salina P WaddyNational Institutes of Health, Bethesda, Maryland, USA.
Kenneth WileyNational Institutes of Health, Bethesda, Maryland, USA.
Andy WilsonUniversity of Utah, Salt Lake City, Utah, USA.
Margot Lisa Jing YannForum for Collaborative Research, School of Public Health, University of California, Berkeley, California, USA.ORCID https://orcid.org/0009-0004-9232-0382
Zhiwei ZhangGilead Sciences, Foster City, California, USA.
Tianyue ZhouDivision of Biostatistics, School of Public Health, University of California, Berkeley, California, USA.
Maya PetersenDivision of Biostatistics, School of Public Health, University of California, Berkeley, California, USA.
Mark van der LaanDivision of Biostatistics, School of Public Health, University of California, Berkeley, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As clinical decision-making increasingly moves toward individualized and context-specific treatment recommendations, reliance on any single evidence source, randomized or observational, may be insufficient. Principled integration of randomized controlled trials and real-world data, grounded in explicit causal frameworks, offers a path toward evidence that is both internally credible and externally relevant. In this article, we describe distinct objectives for the integration of randomized controlled trials and real-world data and discuss how these objectives shape key design and analytic considerations, illustrating the resulting choices through example estimands. We highlight practical issues that commonly arise in applied settings, including data relevance and curation, cross-source comparability, estimand specification, and sensitivity analysis. We aim for this article to help readers evaluate and implement principled approaches to integrating randomized controlled trials and real-world data in ways that can support more reliable treatment recommendations while maintaining regulatory-grade evidentiary standards.

Identifiers

PMID42834704
PMCPMC13639411

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.