Evidence map›Paper›PMID 33650708›Full record

ArticleStatistics in medicine2021

uTPI: A utility-based toxicity probability interval design for phase I/II dose-finding trials.

Haolun Shi, Jiguo Cao, Ying Yuan, Ruitao Lin

Abstract read
In one paragraph

Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
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

9 citing papers in PubMed, 22 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Haolun ShiDepartment of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.ORCID 0000-0002-3620-9717
Jiguo CaoDepartment of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.ORCID 0000-0001-7417-6330
Ying YuanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID 0000-0003-3163-480X
Ruitao LinDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID 0000-0003-2244-131X
Simon Fraser University · CAThe University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Targeting the PI3K Signaling Pathway in Endometrial CarcinomaP50CA098258 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BODURKA, DIANE · 2003 to 2020
$32.0M
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
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
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA261978NIH HHS P30 CA016672NIH HHS P50 CA098258NIH HHS P50 CA221703
6 · The paper itself

Abstract

Unlike chemotherapy, the maximum tolerated dose (MTD) of molecularly targeted agents and immunotherapy may not pose significant clinical benefit over the lower doses. By simultaneously considering both toxicity and efficacy endpoints, phase I/II trials can identify a more clinically meaningful dose for subsequent phase II trials than traditional toxicity-based phase I trials in terms of risk-benefit tradeoff. To strengthen and simplify the current practice of phase I/II trials, we propose a utility-based toxicity probability interval (uTPI) design for finding the optimal biological dose, based on a numerical utility that provides a clinically meaningful, one-dimensional summary representation of the patient's bivariate toxicity and efficacy outcome. The uTPI design does not rely on any parametric specification of the dose-response relationship, and it directly models the dose desirability through a quasi binomial likelihood. Toxicity probability intervals are used to screen out overly toxic dose levels, and then the dose escalation/de-escalation decisions are made adaptively by comparing the posterior desirability distributions of the adjacent levels of the current dose. The uTPI design is flexible in accommodating various dose desirability formulations, while only requiring minimum design parameters. It has a clear decision structure such that a dose-assignment decision table can be calculated before the trial starts and can be used throughout the trial, which simplifies the practical implementation of the design. Extensive simulation studies demonstrate that the proposed uTPI design yields desirable as well as robust performance under various scenarios.

Indexed as

Antineoplastic AgentsBayes TheoremClinical Trials, Phase I as TopicClinical Trials, Phase II as TopicComputer SimulationDose-Response Relationship, DrugHumansMaximum Tolerated DoseModels, StatisticalProbabilityResearch DesignAntineoplastic AgentsBayesian quasi likelihooddose desirabilitydose findingoptimal biological dosephase I/II trialsutility

Identifiers

PMID33650708
PMCPMC13138761
OpenAlexW3134342065

What OpenQuestion holds

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LicenceTDM
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.