ArticleStatistical methods in medical research2020
A surface-free design for phase I dual-agent combination trials.
Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Applying the Partial Order Continual Reassessment Method to High-Dimensional Treatment Combinations.Statistics in medicine · 2026Article
- Bayesian model averaging for partial ordering continual reassessment methods.Biostatistics (Oxford, England) · 2025Article
- Applications of the partial-order continual reassessment method in the early development of treatment combinations.Clinical trials (London, England) · 2024Article
- A comparison of model-free phase I dose escalation designs for dual-agent combination therapies.Statistical methods in medical research · 2024Article
- Local continual reassessment methods for dose finding and optimization in drug-combination trials.Statistical methods in medical research · 2023Article
- Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial.Statistics in medicine · 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
- 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
- Great Wall: A Generalized Dose Optimization Design for Drug Combination Trials Maximizing Survival Benefit.Pharmaceutical statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In oncology, there is a growing number of therapies given in combination. Recently, several dose-finding designs for Phase I dose-escalation trials for combinations were proposed. The majority of novel designs use a pre-specified parametric model restricting the search of the target combination to a surface of a particular form. In this work, we propose a novel model-free design for combination studies, which is based on the assumption of monotonicity within each agent only. Specifically, we parametrise the ratios between each neighbouring combination by independent Beta distributions. As a result, the design does not require the specification of any particular parametric model or knowledge about increasing orderings of toxicity. We compare the performance of the proposed design to the model-based continual reassessment method for partial ordering and to another model-free alternative, the product of independent beta design. In an extensive simulation study, we show that the proposed design leads to comparable or better proportions of correct selections of the target combination while leading to the same or fewer average number of toxic responses in a trial.
Indexed as
Identifiers
What OpenQuestion holds
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.