ArticleStatistics in medicine2015
Competing designs for drug combination in phase I dose-finding clinical trials.
Article in Statistics in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A Novel Method for Inserting Dose Levels Mid-Trial in Early-Phase Oncology Combination Studies.Statistics in medicine · 2026Article
- Bayesian model averaging for partial ordering continual reassessment methods.Biostatistics (Oxford, England) · 2025Article
- Toxicity Adaptive Lists Design: A Practical Design for Phase I Drug Combination Trials in Oncology.JCO precision oncology · 2024Article
- A new function for drug combination dose finding trials.Scientific reports · 2024Article
- A comparison of model-free phase I dose escalation designs for dual-agent combination therapies.Statistical methods in medical research · 2024Article
- Evaluation of phase I clinical trial designs for combinational agents along with guidance based on simulation studies.Journal of applied statistics · 2023Review
- Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial.Statistics in medicine · 2022Article
- A NOVEL FRAMEWORK TO ESTIMATE MULTIDIMENSIONAL MINIMUM EFFECTIVE DOSES USING ASYMMETRIC POSTERIOR GAIN ANDThe annals of applied statistics · 2022Article
- A benchmark for dose-finding studies with unknown ordering.Biostatistics (Oxford, England) · 2022Article
- A dose-finding design for dual-agent trials with patient-specific doses for one agent with application to an opiate detoxification trial.Pharmaceutical statistics · 2022Article
- Accuracy and Safety of Novel Designs for Phase I Drug-Combination Oncology Trials.Statistics in biopharmaceutical research · 2022Article
- Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study.International journal of environmental research and public health · 2021Article
- Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs.BMC medicine · 2020Article
- A surface-free design for phase I dual-agent combination trials.Statistical methods in medical research · 2020Article
- Improving safety of the continual reassessment method via a modified allocation rule.Statistics in medicine · 2020Article
- CRM2DIM: A SAS macro for implementing the dual-agent Bayesian continual reassessment method.Computer methods and programs in biomedicine · 2019Article
- How to design a dose-finding study on combined agents: Choice of design and development of R functions.PloS one · 2019Article
- The changing landscape of phase I trials in oncology.Nature reviews. Clinical oncology · 2016Review
- Practical designs for Phase I combination studies in oncology.Journal of biopharmaceutical statistics · 2016Review
- A comparative study of adaptive dose-finding designs for phase I oncology trials of combination therapies.Statistics in medicine · 2015Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of phase I combination dose-finding studies in oncology is to estimate one or several maximum tolerated doses (MTDs) from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti-tumor action but at the same time also increase the toxicity. It is, however, unreasonable to assume the same dose-toxicity relationship for the combination as for the simple addition of each single agent because of a potential antagonist or synergistic effect. Therefore, using single-agent dose-finding methods for combination therapies is not appropriate. In recent years, several authors have proposed novel dose-finding designs for combination studies, which use either algorithm-based or model-based methods. The aim of our work was to compare, via a simulation study, six dose-finding methods for combinations proposed in recent years. We chose eight scenarios that differ in terms of the number and location of the true MTD(s) in the combination space. We then compared the performance of each design in terms of correct combination selection, patient allocation, and mean number of observed toxicities during the trials. Our results showed that the model-based methods performed better than the algorithm-based ones. However, none of the compared model-based designs gave consistently better results than the others.
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Registered trials
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.