Evidence map›Paper›PMID 39889256›Full record

ArticleJCO clinical cancer informatics2025

CFO: Calibration-Free Odds Bayesian Designs for Dose Finding in Clinical Trials.

Jialu Fang, Ninghao Zhang, Wenliang Wang, Guosheng Yin

Abstract read
In one paragraph

Article in JCO clinical cancer informatics, 2025. 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

4 authors.

Jialu FangSchool of Computing and Data Science, The University of Hong Kong, Hong Kong, China.ORCID 0009-0007-9183-8034
Ninghao ZhangSchool of Computing and Data Science, The University of Hong Kong, Hong Kong, China.ORCID 0009-0004-6071-1442
Wenliang WangSchool of Computing and Data Science, The University of Hong Kong, Hong Kong, China.ORCID 0009-0005-0460-3436
Guosheng YinSchool of Computing and Data Science, The University of Hong Kong, Hong Kong, China.ORCID 0000-0003-3276-1392

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCalibration-free odds type (CFO-type) designs have been demonstrated to be robust, model-free, and practically useful, which have become the state-of-the-art approach for dose finding. However, a key challenge for implementing such designs is a lack of accessible tools. We develop a user-friendly

methodsWe created the

resultsThe CFO package and CFO suite encompass various variants tailored to different clinical settings. Beyond the fundamental CFO design, these include the two-dimensional CFO (2dCFO) for drug-combination trials, accumulative CFO (aCFO) for accommodating all dose information, rCFO for integrating exploration-exploitation via randomization, time-to-event CFO (TITE-CFO), and fractional CFO (fCFO) for addressing late-onset toxicity. Using all information and addressing delayed toxicity outcomes, TITE-aCFO and fractional-aCFO are also included. The package provides functions for determining the subsequent cohort dose, selecting the maximum tolerated dose, and conducting simulations to evaluate performance, with results presented through textual and graphical outputs.

conclusionThe CFO package and CFO suite provide comprehensive and flexible tools for implementing CFO-type designs in phase I clinical trials. This work is highly significant as it integrates all existing CFO-type designs to facilitate novel trial designs with enhanced performance. In addition, this promotes the spread of statistical methods using a user-friendly

Indexed as

Clinical Trials as TopicNeoplasmsResearch DesignSoftwareBayes TheoremCalibrationDose-Response Relationship, DrugHumansMaximum Tolerated Dose

Identifiers

PMID39889256
PMCPMC11797228

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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.