Evidence map›Paper›PMID 35812822›Full record

ReviewContemporary clinical trials communications2022

An overview of the BOIN design and its current extensions for novel early-phase oncology trials.

Revathi Ananthakrishnan, Ruitao Lin, Chunsheng He, Yanping Chen, Daniel Li, Michael LaValley

Abstract readReview
In one paragraph

Review in Contemporary clinical trials communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Tips for Accelerating BOIN Design.Therapeutic innovation & regulatory science · 2024
    Article
  9. Article
  10. 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

6 authors.

Revathi AnanthakrishnanBristol-Myers Squibb (BMS), 300 Connell Drive, Berkeley Heights, NJ, 07922, USA.
Ruitao LinDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Chunsheng HeBristol-Myers Squibb (BMS), 300 Connell Drive, Berkeley Heights, NJ, 07922, USA.
Yanping ChenBristol-Myers Squibb (BMS), 300 Connell Drive, Berkeley Heights, NJ, 07922, USA.
Daniel LiBMS, Seattle, WA, 98109, USA.
Michael LaValleyDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bayesian Optimal Interval (BOIN) designs are a class of model-assisted dose-finding designs that can be used in oncology trials to determine the maximum tolerated dose (MTD) of a study drug based on safety or the optimal biological dose (OBD) based on safety and efficacy. BOIN designs provide a complete suite for dose finding in early phase trials, as well as a consistent way to explore different scenarios such as toxicity, efficacy, continuous outcomes, delayed toxicity or efficacy and drug combinations in a unified manner with easy access to software to implement most of these designs. Although built upon Bayesian probability models, BOIN designs are operationally simple in general and have good statistical operating characteristics compared to other dose-finding designs. This review paper describes the original BOIN design and its many extensions, their advantages and limitations, the software used to implement them, and the most suitable situation for use of each of these designs. Published examples of the implementation of BOIN designs are provided in the Appendix.

Identifiers

PMID35812822
PMCPMC9260438

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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.