Evidence map›Paper›PMID 40705487›Full record

ArticleBiometrics2025

Precision generalized phase I-II designs.

Saijun Zhao, Peter F Thall, Ying Yuan, Juhee Lee, Pavlos Msaouel, Yong Zang

Abstract read
In one paragraph

Article in Biometrics, 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

6 authors.

Saijun ZhaoDepartment of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
Peter F ThallDepartment of Biostatistics, M.D. Anderson Cancer Center, Houston, TX 77030, USA.
Ying YuanDepartment of Biostatistics, M.D. Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0003-3163-480X
Juhee LeeDepartment of Statistics, University of California Santa Cruz, Santa Cruz, CA 95064, USA.ORCID 0000-0002-9787-3830
Pavlos MsaouelDepartments of Genitourinary Medical Oncology and Translational Molecular Pathology, M.D. Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0001-6505-8308
Yong ZangDepartment of Biostatistics and Health Data Science, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.ORCID 0000-0003-0572-4756

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
Tumor Microenvironment and Metastasis ProgramP30CA082709 · NCI · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI David W Clapp · 1999 to 2026
$59.3M
Tropism Enhanced Oncolytic Adenovirus for the Treatment of Brain TumorsP50CA127001 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Juan Fueyo, FREDERICK F LANG · 2008 to 2026
$41.2M
The University of Texas MD Anderson Cancer Center SPORE in Ovarian CancerP50CA281701 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI ROBERT C BAST, ANIL K SOOD · 2023 to 2026
$11.5M
Coordinating and Data Management Center for Translational and Basic Science Research in Early LesionsU24CA274212 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Liang Li, Ying Yuan · 2022 to 2026
$3.5M
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
Use Bayesian methods to facilitate the data integration for complex clinical trialsR01GM150808 · NIGMS · INDIANA UNIVERSITY INDIANAPOLIS · PI Yong Zang · 2023 to 2026
$1.3M
NCI NIH HHS 1R01CA261978NCI NIH HHS 5 P30 CA016672 45NCI NIH HHS P30 CA016672NCI NIH HHS P30 CA082709NCI NIH HHS P50C A127001NCI NIH HHS P50 CA281701NCI NIH HHS P50CA281701NCI NIH HHS R01 CA261978NCI NIH HHS U24C A274212NIGMS NIH HHS R01 GM150808NIH HHS P30 CA082709NIH HHS R01 GM150808
6 · The paper itself

Abstract

A new family of precision Bayesian dose optimization designs, PGen I-II, based on early efficacy, early toxicity, and long-term time to treatment failure is proposed. A PGen I-II design refines a Gen I-II design by accounting for patient heterogeneity characterized by subgroups that may be defined by prognostic levels, disease subtypes, or biomarker categories. The design makes subgroup-specific decisions, which may be to drop an unacceptably toxic or inefficacious dose, randomize patients among acceptable doses, or identify a best dose in terms of treatment success defined in terms of time to failure over long-term follow-up. A piecewise exponential distribution for failure time is assumed, including subgroup-specific effects of dose, response, and toxicity. Latent variables are used to adaptively cluster subgroups found to have similar dose-outcome distributions, with the model simplified to borrow strength between subgroups in the same cluster. Guidelines and user-friendly computer software for implementing the design are provided. A simulation study is reported that shows the PGen I-II design is superior to similarly structured designs that either assume patient homogeneity or conduct separate trials within subgroups.

Indexed as

Clinical Trials, Phase I as TopicClinical Trials, Phase II as TopicResearch DesignBayes TheoremBiometryComputer SimulationDose-Response Relationship, DrugHumansModels, StatisticalBayesian adaptive designcell therapydose optimizationphase I-II clinical trialsprecision medicine

Identifiers

PMID40705487
PMCPMC12288667

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.