Evidence map›Paper›PMID 38111231›Full record

ArticleClinical trials (London, England)2024

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.

Pavlos Msaouel, Juhee Lee, Peter F Thall

Open access · hybridAbstract read
In one paragraph

Article in Clinical trials (London, England), 2024. 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
1.0field-weighted citation impact, top 21% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Estimands in clinical trials of complex disease processes.Clinical trials (London, England) · 2024
    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 at 2 institutions in 1 country.

Pavlos MsaouelDepartment of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0001-6505-8308
Juhee LeeDepartment of Statistics, University of California Santa Cruz, Santa Cruz, CA, USA.
Peter F ThallDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0002-7293-529X
The University of Texas MD Anderson Cancer Center · USUniversity of California, Santa Cruz · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
STATISTICAL METHODS FOR COMPLEX CANCER TRIALSR01CA083932 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI THALL, PETER F · 2000 to 2017
$2.3M
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 CA083932NCI NIH HHS R01 CA261978
6 · The paper itself

Abstract

backgroundIdentifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life. PURPOSE: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups. METHODS AND

conclusionsHerein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Indexed as

Clinical Trials, Phase I as TopicClinical Trials, Phase II as TopicResearch DesignAntineoplastic AgentsCarcinoma, Renal CellDose-Response Relationship, DrugHumansKidney NeoplasmsPrognosisQuality of LifeRisk AssessmentAntineoplastic AgentsCovariate-specific utilitiespersonalized medicinephase I-II trialsprognostic subgroupsrisk–benefit trade-offsutility functions

Identifiers

PMID38111231
PMCPMC11132955
OpenAlexW4389941858

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.