Evidence map›Paper›PMID 39989040›Full record

ArticleJournal of the National Cancer Institute. Monographs2025

On the use of external controls in clinical trials.

Michelle M Nuño, Stephanie L Pugh, Lingyun Ji, Jin Piao, James J Dignam, Jon A Steingrimsson

Abstract read
In one paragraph

Article in Journal of the National Cancer Institute. Monographs, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

6 authors.

Michelle M NuñoChildren's Oncology Group, Monrovia, CA 91016, United States.ORCID 0000-0003-2031-929X
Stephanie L PughNRG Oncology Statistics and Data Management Center, American College of Radiology, Philadelphia, PA 19103, United States.
Lingyun JiChildren's Oncology Group, Monrovia, CA 91016, United States.
Jin PiaoChildren's Oncology Group, Monrovia, CA 91016, United States.
James J DignamNRG Oncology Statistics and Data Management Center, American College of Radiology, Philadelphia, PA 19103, United States.
Jon A SteingrimssonDepartment of Biostatistics, Brown University, Providence, RI 02903, United States.

Funding

NCTN BIQSFP ANBL1531 (NRT)U10CA180886 · NCI · PUBLIC HEALTH INSTITUTE · PI Douglas S. Hawkins · 2014 to 2026
$390.6M
ECOG-ACRIN NCORP Research Base - TMISTUG1CA189828 · NCI · ECOG-ACRIN MEDICAL RESEARCH FOUNDATION · PI Peter J ODwyer, MITCHELL D. SCHNALL · 2014 to 2026
$199.7M
Project-006U10CA180820 · NCI · ECOG-ACRIN MEDICAL RESEARCH FOUNDATION · PI Peter J ODwyer · 2014 to 2026
$167.6M
NRG Oncology NCORP Research Base-BIQSFPUG1CA189867 · NCI · NRG ONCOLOGY FOUNDATION, INC. · PI Deborah Watkins Bruner, Lisa A. Kachnic · 2014 to 2026
$140.5M
COG SDMC - Statistics CoreU10CA180899 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TODD A ALONZO · 2014 to 2026
$132.8M
Collaborations and NCORP Collective ManagementUG1CA189823 · NCI · MAYO CLINIC ROCHESTER · PI Evanthia Galanis, Olwen Hahn · 2014 to 2026
$120.6M
Statistics CoreU10CA180794 · NCI · DANA-FARBER CANCER INST · PI PAUL J CATALANO, CONSTANTINE A. GATSONIS · 2014 to 2026
$111.2M
SWOG NCORP Research BaseUG1CA189974 · NCI · THE HOPE FOUNDATION · PI CHARLES D. BLANKE, DAWN HERSHMAN · 2014 to 2026
$80.6M
URCC NCORP Research BaseUG1CA189961 · NCI · UNIVERSITY OF ROCHESTER · PI Michelle C Janelsins, KAREN M. MUSTIAN · 2014 to 2026
$67.7M
Towards a preventive cancer vaccine for children with constitutional mismatch repair deficiencyUG1CA189955 · NCI · PUBLIC HEALTH INSTITUTE · PI BRAD H POLLOCK, Michael E. Roth · 2014 to 2026
$62.5M
Wake Forest NCORP Research BaseUG1CA189824 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Emily Van Meter Dressler, GLENN J LESSER · 2014 to 2026
$57.9M
ACRIN UG1CA189828Children's Oncology GroupChildren's Oncology Group 5UG1CA189955-11Community Oncology Research Program UG1CA189867ECOGNCI NIH HHS U10 CA180794NCI NIH HHS U10 CA180820NCI NIH HHS U10 CA180886NCI NIH HHS U10 CA180899NCI NIH HHS UG1 CA189823NCI NIH HHS UG1 CA189824NCI NIH HHS UG1 CA189828NCI NIH HHS UG1 CA189867NCI NIH HHS UG1 CA189955NCI NIH HHS UG1 CA189961NCI NIH HHS UG1 CA189974NIH HHS U10CA180886University of Rochester UG1CA189961Wake Forest University UG1CA189824
6 · The paper itself

Abstract

Externally controlled trials have commonly been used when conducting a randomized controlled trial (RCT) is not feasible or ethical. By allowing the study of new treatments, use of external controls can lead to accelerated advances in the management of rare diseases. The use of external controls, however, introduces new challenges due to potential differences between the population the external controls are enrolled from and the population the patients on the new trial are enrolled from. Some differences include, but are not limited to, differences in how patients are diagnosed and treated, differences in the case mix of the underlying populations, differences in the ability to measure outcomes, and differences in data collection. We discuss the potential benefits and challenges of externally controlled trials, as well as strategies to mitigate bias, including the estimand and target-trial emulation framework. We also provide a brief overview of statistical methodology commonly used in these settings. We note that although the strategies presented may help mitigate some of these challenges, they cannot replace an RCT framework, and investigators should be aware of the potential limitations of externally controlled trials.

Indexed as

Clinical Trials as TopicNeoplasmsRandomized Controlled Trials as TopicResearch DesignBiasControl GroupsHumans

Identifiers

PMID39989040
PMCPMC11848027

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.