Evidence map›Paper›PMID 38033501›Full record

SynthesisFrontiers in oncology2023

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity.

Adetayo Kasim, Nathan Bean, Sarah Jo Hendriksen, Tai-Tsang Chen, Helen Zhou, Matthew A Psioda

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 5 pooled it
–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

17 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  16. Dual inhibition of HERs and PD-1 counteract resistance in KRASJournal of experimental & clinical cancer research : CR · 2024
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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.

Adetayo KasimDisease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Brentford, United Kingdom.
Nathan BeanStatistics and Data Science - Innovation Hub, GlaxoSmithKline, Philadelphia, PA, United States.
Sarah Jo HendriksenMedical and Market Access, Oncology Biostatistics, GlaxoSmithKline, Stevenage, United Kingdom.
Tai-Tsang ChenDisease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Philadelphia, PA, United States.
Helen ZhouDisease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Philadelphia, PA, United States.
Matthew A PsiodaStatistics and Data Science - Innovation Hub, GlaxoSmithKline, Philadelphia, PA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Basket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials. Methods: Innovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development. Results: We found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial. Conclusion: Innovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods.

Indexed as

basket trialsBayesian model averaginginformation borrowingtumor-agnostic treatment effecttumor types

Identifiers

PMID38033501
PMCPMC10684308

What OpenQuestion holds

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LicenceCC BY
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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.