ArticleBMC medical research methodology2021
Phase I dose-escalation oncology trials with sequential multiple schedules.
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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Who cites it
8 citing papers in PubMed.
- An Overview on T-Cell Engagers: The Current Position in Both the Biological and Mathematical Context.Computational and structural biotechnology journal · 2026Review
- Autologous Bone Marrow Aspiration Concentrate (BMAC) Therapy for Primary Knee Osteoarthritis-An Observational and Dose Escalation Study.Indian journal of orthopaedics · 2024Article
- Dose optimization for cancer treatments with considerations for late-onset toxicities.Clinical trials (London, England) · 2024Article
- An extended Bayesian semi-mechanistic dose-finding design for phase I oncology trials using pharmacokinetic and pharmacodynamic information.Statistics in medicine · 2024Article
- Development of consensus-driven SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study.BMC medicine · 2023Review
- Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial.Statistics in medicine · 2022Article
- A semi-mechanistic dose-finding design in oncology using pharmacokinetic/pharmacodynamic modeling.Pharmaceutical statistics · 2022Article
- Exposure driven dose escalation design with overdose control: Concept and first real life experience in an oncology phase I trial.Contemporary clinical trials communications · 2022Article
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Authors and funding
4 authors.
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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.
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