Evidence map›Paper›PMID 40059637›Full record

ArticleClinical pharmacology and therapeutics2025

Evolving Recommendations for Patient Populations Among Oncology Medicines: A Quantitative and Qualitative Analysis.

Milou A Hogervorst, Rick A Vreman, Theresa A Oduol, Aukje K Mantel-Teeuwisse, Wim G Goettsch, Aaron S Kesselheim

Abstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2025. 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

6 authors.

Milou A HogervorstDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands.ORCID 0000-0001-7528-574X
Rick A VremanDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands.
Theresa A OduolDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Program On Regulation, Therapeutics, And Law (PORTAL), Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Aukje K Mantel-TeeuwisseDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands.ORCID 0000-0002-8782-0698
Wim G GoettschDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands.
Aaron S KesselheimDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Program On Regulation, Therapeutics, And Law (PORTAL), Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-8867-2666

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

After a medicine has been tested in pivotal trials, regulators, health technology assessment (HTA) organizations, and professional societies make decisions about the patients best served by the medicine. This study assesses how the patient populations for oncology medicines (2010-2023) are defined (1) at trial, (2) regulatory submission, (3) upon approval for marketing authorization, (4) at submission, and (5) recommendation by the HTA, and (6) in clinical guidelines in Australia, Canada, the Netherlands, the United Kingdom, and the United States. Based on 25 populations for oncology medicines, we developed a framework for describing oncology populations consisting of 20 elements in four domains: disease specifications, patient characteristics, treatment position, and exclusion criteria. In exploratory analyses, we tabulated any observed variation in these framework elements throughout the six steps in the lifecycle of a medicine. On average, 10 (95% confidence interval [CI]: 9.2-10.9) potential adjustments were made, 2.3 (95% CI: 2.0-2.5) by each decision-maker. The adjustments by pharmaceutical developers focused mostly on the disease specifications (0.5 of the average 0.8 adjustments, 63%), while adjustments by regulators, HTA organizations, and guideline developers predominantly targeted the treatment's position (range: 0.5/1.3 [36%] in guidelines to 0.6/1.0 [58%] in regulatory approvals). Each decision-maker on average modifies 1.0 element (out of 2.3 [43%]) that was previously adjusted by another decision-maker. The multiple differences observed in the description of patient populations reflect inconsistency in reporting between decision-makers, complicating communication to patients and potentially affecting access to medicines. The developed framework can support consistent reporting across stakeholders and countries.

Indexed as

Antineoplastic AgentsNeoplasmsAustraliaDrug ApprovalHumansPractice Guidelines as TopicTechnology Assessment, BiomedicalUnited StatesAntineoplastic Agents

Identifiers

PMID40059637
PMCPMC12166271

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.