Evidence map›Paper›PMID 26486139›Full record

ArticleBiostatistics (Oxford, England)2016

A robust Bayesian dose-finding design for phase I/II clinical trials.

Suyu Liu, Valen E Johnson

Abstract read
In one paragraph

Article in Biostatistics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

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  19. CFO: Calibration-free odds design for phase I/II clinical trials.Statistical methods in medical research · 2022
    Article
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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

2 authors.

Suyu LiuDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA syliu@mdanderson.org.
Valen E JohnsonDepartment of Statistics, Texas A&M University, College Station, TX, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
Consistent variable selection in p>>n settingsR01CA158113 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JOHNSON, VALEN EARL · 2011 to 2020
$2.8M
Bayesian Adaptive Designs for Oncology Clinical Trials with Late-onset OutcomesR01CA154591 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI YUAN, YING · 2011 to 2014
$1.2M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA154591NCI NIH HHS R01 CA158113
6 · The paper itself

Abstract

We propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves.

Indexed as

Bayes TheoremClinical Trials, Phase I as TopicClinical Trials, Phase II as TopicDose-Response Relationship, DrugResearch DesignHumansAdaptive designBayesian methodPhase I/II trialTrade-offUtility

Identifiers

PMID26486139
PMCPMC5006115

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.