Evidence map›Paper›PMID 39989043›Full record

ArticleJournal of the National Cancer Institute. Monographs2025

Design and analysis considerations for investigating patient subgroups of interest within cancer clinical trials.

Gina L Mazza, Eva Culakova, Danielle M Enserro, James J Dignam, Joseph M Unger

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 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. 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

5 authors.

Gina L MazzaAlliance Statistics and Data Management Center, Mayo Clinic, Scottsdale, AZ, United States.ORCID 0000-0002-5305-6193
Eva CulakovaDivision of Supportive Care in Cancer, Department of Surgery, University of Rochester Medical Center, Rochester, NY, United States.
Danielle M EnserroNRG Oncology Statistics and Data Management Center, Philadelphia, PA, United States.ORCID 0000-0003-2132-9967
James J DignamNRG Oncology Statistics and Data Management Center, Philadelphia, PA, United States.
Joseph M UngerSWOG Statistics and Data Management Center, Fred Hutchinson Cancer Center, Seattle, WA, United States.

Funding

ECOG-ACRIN NCORP Research Base - TMISTUG1CA189828 · NCI · ECOG-ACRIN MEDICAL RESEARCH FOUNDATION · PI Peter J ODwyer, MITCHELL D. SCHNALL · 2014 to 2026
$199.7M
Statistics CoreU10CA180822 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI James J. Dignam, GREG YOTHERS · 2014 to 2026
$146.4M
Collaborations and NCORP Collective ManagementUG1CA189823 · NCI · MAYO CLINIC ROCHESTER · PI Evanthia Galanis, Olwen Hahn · 2014 to 2026
$120.6M
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 Group 5UG1CA189955-11ECOGNCI Community Oncology Research ProgramNCI NIH HHS U10 CA180822NCI NIH HHS UG1 CA189823NCI NIH HHS UG1 CA189824NCI NIH HHS UG1 CA189828NCI NIH HHS UG1 CA189955NCI NIH HHS UG1 CA189961NCI NIH HHS UG1 CA189974NIH HHS UG1CA189823NRG Oncology UG1CA189867SWOG UG1CA189974University of Rochester UG1CA189961Wake Forest University UG1CA189824
6 · The paper itself

Abstract

Examining treatment effects in subgroups of patients defined by demographic, genetic, or clinical characteristics is increasingly of interest given the pursuit of personalized medicine and the importance of representation and equity in treatment decisions. The magnitude or even the direction of the treatment effect may vary across subgroups, and these differential treatment effects could have clinical implications. Subgroup analyses require caution in their interpretation, however, because of the high probability of a false-positive or false-negative conclusion. We outline study design and analysis considerations for responsibly investigating and reporting differential treatment effects across subgroups in oncology trials, with examples from the National Cancer Institute's National Clinical Trials Network and Community Oncology Research Program. Recommendations include ensuring appropriate representation of patients from subgroups of interest, recognizing power and multiplicity limitations, and treating exploratory subgroup analyses as hypothesis generating rather than practice changing.

Indexed as

Clinical Trials as TopicNeoplasmsResearch DesignHumansPrecision MedicineUnited States

Identifiers

PMID39989043
PMCPMC11848030

What Socratic 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 Socratic graph.