Evidence map›Paper›PMID 42262756›Full record

ArticleJAMA network open2026

Patient Perspectives on Approval Speed vs Evidentiary Certainty in US Cancer Drug Approvals.

Robin Forrest, Ajay Aggarwal, Michelle Tregear, Melanie Wyne, Holly Fernandez Lynch, Anita K Wagner, Emily Jackson, Huseyin Naci

Abstract read
In one paragraph

Article in JAMA network open, 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

8 authors.

Robin ForrestDepartment of Health Policy, London School of Economics and Political Science, London, United Kingdom.
Ajay AggarwalDepartment of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, Department of Oncology, Guy's and St Thomas' NHS Trust, London, United Kingdom.
Michelle TregearNational Breast Cancer Coalition, Washington, DC.
Melanie WyneNational Breast Cancer Coalition, Washington, DC.
Holly Fernandez LynchDepartment of Medical Ethics and Health Policy, The Perelman School of Medicine, Carey Law School, University of Pennsylvania, Philadelphia.
Anita K WagnerDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
Emily JacksonLaw School, London School of Economics and Political Science, London, United Kingdom.
Huseyin NaciDepartment of Health Policy, London School of Economics and Political Science, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Use of the US Food and Drug Administration's Accelerated Approval pathway assumes that patients are willing to accept uncertainty about clinical benefit in exchange for faster access to new cancer drugs. However, little is known about how patients view this trade-off or the circumstances under which they consider it acceptable. Objectives: To explore patients' views on the trade-off between faster approval and evidentiary certainty regarding the clinical benefit of new cancer drugs in the US. Design, Setting, and Participants: This qualitative study used semistructured interviews conducted online between January 27 and April 1, 2025, with patients aged 18 years or older diagnosed with breast cancer. Purposive sampling was used to achieve maximum variation in cancer stage, treatment status, age, and sociodemographic background. Main Outcomes and Measures: The primary outcome was patients' views on the trade-off between faster drug approval and evidentiary certainty. Interview transcripts were thematically analyzed to identify key patterns in understanding, experiences of uncertainty, attitudes toward waiting, treatment priorities, and views on regulatory decision-making. Results: A total of 125 individuals registered their interest and met eligibility criteria to participate. After purposive sampling, 30 participated in this study (10 aged 20-39 years [33.3%], 12 aged 40-59 years [40.0%], and 8 aged >60 years [26.7%]; all female), representing 17 US states (9 Northeast, 12 Midwest, 4 South, 5 West). A total of 13 participants (43.3%) had metastatic disease, 21 (70.0%) were receiving active treatment, and 27 (90.0%) had received at least 1 systemic therapy. Faster approval at the expense of certainty about the clinical benefit of new cancer drugs was considered most acceptable when no alternative treatments existed or when the anticipated benefits were transformative. When clinical benefit was uncertain, participants emphasized the importance of survival and quality of life as priority treatment outcomes, the added risks of adverse effects, and the burden of intensive treatment. Many viewed broader access to clinical trials, rather than faster regulatory approval, as an effective way to address unmet needs while facilitating evidence generation for approval. Conclusion and Relevance: In this qualitative study of patients diagnosed with breast cancer, there was a mismatch between when patients considered trading evidentiary certainty for faster approval as acceptable and the conditions under which many new cancer drugs are approved by the Food and Drug Administration. To better align regulatory approvals with patient values, use of accelerated approval may be most appropriate for drugs that are likely to address genuine treatment gaps or offer meaningful improvements in clinical outcomes over existing alternatives.

Indexed as

Antineoplastic AgentsBreast NeoplasmsDrug ApprovalAdultAgedFemaleHumansMiddle AgedQualitative ResearchUncertaintyUnited StatesUnited States Food and Drug AdministrationYoung AdultAntineoplastic Agents

Identifiers

PMID42262756
PMCPMC13250706

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Registered trials

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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.