Evidence map›Paper›PMID 38317370›Full record

ArticlePharmaceutical statistics

On the relative conservativeness of Bayesian logistic regression method in oncology dose-finding studies.

Cheng-Han Yang, Guanghui Cheng, Ruitao Lin

Abstract read
In one paragraph

Article in Pharmaceutical statistics. 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. 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

3 authors.

Cheng-Han YangDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Guanghui ChengGuangzhou Institute of International Finance, Guangzhou University, Guangzhou, Guangdong, China.
Ruitao LinDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID 0000-0003-2244-131X

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
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
Bayesian Methods for Complex Precision Biotherapy Trials in OncologyR01CA261978 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIN, RUITAO, THALL, PETER F · 2021 to 2024
$1.4M
GuangDong Basic and Applied Basic Research Foundation 2022A1515011899National Nature Science Foundation of China 12001128NCI NIH HHS 1R01CA261978NCI NIH HHS 5P30CA016672NCI NIH HHS 5P50CA221703NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA221703NCI NIH HHS R01 CA261978
6 · The paper itself

Abstract

The Bayesian logistic regression method (BLRM) is a widely adopted and flexible design for finding the maximum tolerated dose in oncology phase I studies. However, the BLRM design has been criticized in the literature for being overly conservative due to the use of the overdose control rule. Recently, a discussion paper titled "Improving the performance of Bayesian logistic regression model with overall control in oncology dose-finding studies" in Statistics in Medicine has proposed an overall control rule to address the "excessive conservativeness" of the standard BLRM design. In this short communication, we discuss the relative conservativeness of the standard BLRM design and also suggest a dose-switching rule to further enhance its performance.

Indexed as

Antineoplastic AgentsBayes TheoremClinical Trials, Phase I as TopicDose-Response Relationship, DrugMaximum Tolerated DoseHumansLogistic ModelsNeoplasmsResearch DesignAntineoplastic AgentsBayesian logistic regression modeldose findingmaximum tolerated doseoverdose controlphase I trial

Identifiers

PMID38317370
PMCPMC11789473

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.