Evidence map›Paper›PMID 33853539›Full record

ArticleBMC medical research methodology2021

Phase I dose-escalation oncology trials with sequential multiple schedules.

Burak Kürsad Günhan, Sebastian Weber, Abdelkader Seroutou, Tim Friede

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Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Burak Kürsad GünhanDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany. burak.gunhan@med.uni-goettingen.de.ORCID 0000-0002-7454-8680
Sebastian WeberNovartis Pharma AG, Basel, Switzerland.
Abdelkader SeroutouNovartis Pharma AG, Basel, Switzerland.
Tim FriedeDepartment of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.ORCID 0000-0001-5347-7441

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.

methodsHere, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model.

resultsIn a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ).

conclusionIn phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.

Indexed as

NeoplasmsBayes TheoremComputer SimulationDose-Response Relationship, DrugHumansMaximum Tolerated DoseMedical OncologyResearch DesignBayesian statisticsMultiple treatment schedulesPhase I dose-escalation trialsPK models

Identifiers

PMID33853539
PMCPMC8045405

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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.