Evidence map›Paper›PMID 39222294›Full record

ArticleTherapeutic innovation & regulatory science2024

Tips for Accelerating BOIN Design.

Masahiro Kojima, Wu Wende, Henry Zhao

Abstract read
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In one paragraph

Article in Therapeutic innovation & regulatory science, 2024. 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

3 authors.

Masahiro KojimaBiometrics Department, Research Division, Kyowa Kirin Co., Ltd. Otemachi Financial City Grand Cube, 1-9-2 Otemachi, Chiyoda-ku, Tokyo, 100-004, Japan. kojima.masahiro@ism.ac.jp.
Wu WendeBiometrics Department, Development Division, Kyowa Kirin, Inc, Princeton, NJ, USA.
Henry ZhaoBiometrics Department, Development Division, Kyowa Kirin, Inc, Princeton, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During discussions at the Data Science Roundtable meeting in Japan, there were instances where the adoption of the BOIN design was declined, attributed to the extension of study duration and increased sample size in comparison to the 3 + 3 design. We introduce an accelerated BOIN design aimed at completing a clinical phase I trial at a pace comparable to the 3 + 3 design. Additionally, we introduce how we could have applied the BOIN design within our company, which predominantly utilized the 3 + 3 design for most of its clinical oncology dose escalation trials. The accelerated BOIN design is adaptable by using efficiently designated stopping criterion for the existing BOIN framework. Our approach is to terminate the dose escalation study if the number of evaluable patients treated at the current dose reaches 6 and the decision is to stay at the current dose for the next cohort of patients. In addition, for lower dosage levels, considering a cohort size smaller than 3 may be feasible when there are no safety concerns from non-clinical studies. We demonstrate the accelerated BOIN design using a case study and subsequently evaluate the performance of our proposed design through a simulation study. In the simulation study, the average difference in the percentage of correct MTD selection between the accelerated BOIN design and the standard BOIN design was - 2.43%, the average study duration and the average sample size of the accelerated BOIN design was reduced by 14.8 months and 9.22, respectively, compared with the standard BOIN design.

Indexed as

Research DesignAntineoplastic AgentsClinical Trials, Phase I as TopicComputer SimulationDose-Response Relationship, DrugHumansMaximum Tolerated DoseSample SizeAntineoplastic Agents3 + 3 DesignAccelerated BOIN design

Identifiers

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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.