Evidence map›Paper›PMID 34249223›Full record

ArticleStatistics in biopharmaceutical research2021

A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials.

Yanhong Zhou, Ruobing Li, Fangrong Yan, J Jack Lee, Ying Yuan

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in biopharmaceutical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

2 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Phase I CAR-T Clinical Trials Review.Anticancer research · 2022
    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

5 authors at 3 institutions in 2 countries.

Yanhong ZhouDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ruobing LiThe Center for Drug Evaluation, Beijing, China.
Fangrong YanChina Pharmaceutical University, Nanjing, China.
J Jack LeeDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ying YuanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
The University of Texas MD Anderson Cancer Center · USChina Pharmaceutical University · CNNational Medical Products Administration · CN

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Tropism Enhanced Oncolytic Adenovirus for the Treatment of Brain TumorsP50CA127001 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Juan Fueyo · 2008 to 2026
$41.2M
Project 3: Inhibiting Oxidative Phosphorylation in Pancreatic CancerP50CA221707 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI KOPETZ, SCOTT, MAITRA, ANIRBAN · 2019 to 2023
$11.0M
The University of Texas MD Anderson Cancer Center SPORE in MelanomaP50CA221703 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DAVIES, MICHAEL · 2019 to 2023
$10.3M
NCI Diversity Supplement: Glioma therapy with oncolytic adenoviruses and immunometabolic adjuvantsR01CA256006 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI FUEYO, JUAN, GOMEZ-MANZANO, CANDELARIA · 2021 to 2025
$2.0M
NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA127001NCI NIH HHS P50 CA221703NCI NIH HHS P50 CA221707NCI NIH HHS R01 CA256006
6 · The paper itself

Abstract

Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.

Indexed as

Algorithm-based designDose findingModel-assisted designPhase I trials

Identifiers

PMID34249223
PMCPMC8261789
OpenAlexW3079983042

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.