Evidence map›Paper›PMID 23856381›Full record

ArticleContemporary clinical trials2013

Up-and-down designs for phase I clinical trials.

Suyu Liu, Chunyan Cai, Jing Ning

Abstract read
In one paragraph

Article in Contemporary clinical trials, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.Clinical cancer research : an official journal of the American Association for Cancer Research · 2016
    Article
  11. Article
  12. Challenges to implement minimum effective volume in regional anesthesia.Journal of anaesthesiology, clinical pharmacology
    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.

Suyu LiuDepartment of Biostatistics, The University of Texas M. D. Anderson Cancer Center, USA. syliu@mdanderson.org
Chunyan Cai
Jing Ning

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Kathrin Milbury · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

Various up-and-down designs have been proposed to improve the operating characteristics of the traditional "3 + 3" design, but they have been of limited use in practice. A major impediment to the adoption of the improved up-and-down designs is a lack of general guidance and a comprehensive assessment of the operating characteristics of these designs under practical clinical settings. To fill this gap, we review six up-and-down designs: the "3 + 3" design, accelerated titration design, biased coin design, k-in-a-row design, group up-and-down design and cumulative group up-and-down design. We conduct comprehensive simulation studies to evaluate their operating characteristics under various practical settings, and compare their performance to a theoretical optimal bound of nonparametric designs. The results show that the cumulative group up-and-down design has the best overall performance in terms of selecting the maximum tolerated dose (MTD), assigning patients to the MTD and patient safety. Its performance is generally close to the upper bound of nonparametric designs, but improvement seems possible in some cases.

Indexed as

Maximum Tolerated DoseModels, StatisticalResearch DesignAlgorithmsBiasClinical Trials, Phase I as TopicComputer SimulationDose-Response Relationship, DrugHumansDose findingMaximum tolerated doseOptimal designUpper bound

Identifiers

PMID23856381
PMCPMC4217164

What OpenQuestion holds

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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.