Evidence map›Paper›PMID 41394948›Full record

ArticleJournal of the American Statistical Association2025

Posterior Predictive Design for Phase I Clinical Trials.

Chenqi Fu, Shouhao Zhou, J Jack Lee

Abstract read
In one paragraph

Article in Journal of the American Statistical Association, 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

3 authors.

Chenqi FuDivision of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University, Hershey, PA.ORCID 0000-0001-6387-6358
Shouhao ZhouDivision of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University, Hershey, PA.ORCID 0000-0002-8124-5047
J Jack LeeDepartment of Biostatistics, The University of Texas - MD Anderson Cancer Center, Houston, TX.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
The Penn State Coordinating Center (CC) for the Multi-Sectoral Preventive Interventions (MSPI) Research NetworkU24MD020517 · NIMHD · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI CHINCHILLI, VERNON M, KRASCHNEWSKI, JENNIFER L. · 2025 to 2025
$3.3M
Meta-analysis of Adverse Drug Effects in Clinical TrialsR21LM014534 · NLM · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI PETERSON, CHRISTINE B, ZHOU, SHOUHAO · 2024 to 2025
$432k
NCI NIH HHS P30 CA016672NIMHD NIH HHS U24 MD020517NLM NIH HHS R21 LM014534
6 · The paper itself

Abstract

Interval-based designs represent cutting-edge adaptive methodologies for phase I clinical trials to identify the maximum tolerated dose (MTD). These designs exhibit robust performance comparable to more intricate, model-based designs, and their pretabulated decision rule enables them to be implemented as simply as the conventional algorithm-based designs. In this paper, we introduce the posterior predictive (PoP) design, a novel interval-based design that leverages advanced Bayesian predictive hypothesis testing techniques for dose escalation and de-escalation. Our work moves beyond the existing model-assisted interval-based designs by achieving global optimality in dose transition. Theoretically, the global optimality ensures that the proposed design can consistently select the true MTD at an impressive convergence rate of

Indexed as

BOIN designInterval-based designsModel-assisted designPoP designPredictive Bayes factor

Identifiers

PMID41394948
PMCPMC12700609

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.