Evidence map›Paper›PMID 40324518›Full record

ArticleContemporary clinical trials2025

Assessing the role of correlation between efficacy and toxicity endpoints in the performance of Bayesian optimal phase II design.

Xun Xu, Ying Yuan, J Jack Lee

Abstract read
In one paragraph

Article in Contemporary clinical trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Xun XuDepartment of Biostatistics and Data Science, School of Public Health, University of Texas, Houston 77030, TX, United States; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston 77030, TX, United States.
Ying YuanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston 77030, TX, United States.
J Jack LeeDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston 77030, TX, United States. Electronic address: jjlee@mdanderson.org.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Tropism Enhanced Oncolytic Adenovirus for the Treatment of Brain TumorsP50CA127001 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Juan Fueyo · 2008 to 2026
$41.2M
Translational Cancer Immune Monitoring and Analysis Center (TCIMAC)U24CA224285 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Gheath Al-Atrash, Cara L Haymaker · 2017 to 2026
$16.7M
The University of Texas MD Anderson Cancer Center SPORE in Ovarian CancerP50CA281701 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Ying Yuan · 2023 to 2026
$11.5M
The University of Texas MD Anderson Cancer Center SPORE in MelanomaP50CA221703 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DAVIES, MICHAEL · 2019 to 2023
$10.3M
Project 3: OPC-RADP01CA285249 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Carly E. A. Barbon · 2024 to 2026
$9.4M
PASSCODE (Pancreatic Adenocarcinoma Stromal Reprograming ConSortium COordination, Data Management and Education)U24CA274274 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI J. Jack LEE, ANIRBAN MAITRA · 2022 to 2026
$4.9M
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
NCI NIH HHS P01 CA285249NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA127001NCI NIH HHS P50 CA221703NCI NIH HHS P50 CA281701NCI NIH HHS U24 CA224285NCI NIH HHS U24 CA274212NCI NIH HHS U24 CA274274
6 · The paper itself

Abstract

Bayesian optimal phase II (BOP2) design serves to screen the efficacy and/or toxicity of a new treatment, determining whether it warrants further development. When employing the BOP2 design to jointly monitor the efficacy-toxicity tradeoff, the correlation between them plays an important role in achieving good performance on the statistical power while controlling Type I error. However, in practice, this correlation is usually unknown, posing challenges in the trial design and data analysis. In this study, the phi coefficient is chosen to measure the correlation. The influence of the efficacy-toxicity tradeoff on the power of BOP2 designs is first evaluated in the design stage and the data analysis stage, separately, then, overall considerations are given. In the design stage, we observe that the power increases as phi increases. Upon performing the sensitivity analysis in the data analysis stage, we find that the power decreases as phi increases given the pre-determined stopping boundaries. Simulations demonstrate instances of overpowering with inflated Type I error when the assumed phi in the design stage exceeds the true phi in the data analysis stage. Conversely, underpowering occurs with controlled Type I error when the assumed phi is lower than the true phi in the data analysis stage. To obtain the power under controlled Type I error in design, we recommend the use of independent correlation when the efficacy and toxicity are likely to be positively correlated. Conversely, for cases of likely negative correlation, taking a phi value close to the lower bound is advisable.

Indexed as

Clinical Trials, Phase II as TopicResearch DesignBayes TheoremComputer SimulationEndpoint DeterminationHumansTreatment OutcomeBayesian adaptive designData analysis stageDesign stageEfficacy-toxicity tradeoffPhi coefficientSensitivity analysis

Identifiers

PMID40324518
PMCPMC13353185

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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.