ArticleStatistical methods in medical research2024
A comparison of model-free phase I dose escalation designs for dual-agent combination therapies.
Article in Statistical methods in medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Novel Method for Inserting Dose Levels Mid-Trial in Early-Phase Oncology Combination Studies.Statistics in medicine · 2026Article
- A short review of dose-finding adaptive trial designs for clinical pharmacologists and non-statisticians in oncology.European journal of clinical pharmacology · 2026Review
- Joint time-to-event partial order continual reassessment method and Joint time-to-event Bayesian logistic regression model: Statistical designs for dual agent phase I/II dose finding studies with late-onset toxicity and activity outcomes.Statistical methods in medical research · 2026Article
- Bayesian model averaging for partial ordering continual reassessment methods.Biostatistics (Oxford, England) · 2025Article
- Seamless monotherapy-combination phase I dose-escalation model-based design.Clinical trials (London, England) · 2025Article
- A clinical phase I dose-finding design with adaptive shrinking boundaries for drug combination trials.BMC medical research methodology · 2023Article
- Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial.Statistics in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
It is increasingly common for therapies in oncology to be given in combination. In some cases, patients can benefit from the interaction between two drugs, although often at the risk of higher toxicity. A large number of designs to conduct phase I trials in this setting are available, where the objective is to select the maximum tolerated dose combination. Recently, a number of model-free (also called model-assisted) designs have provoked interest, providing several practical advantages over the more conventional approaches of rule-based or model-based designs. In this paper, we demonstrate a novel calibration procedure for model-free designs to determine their most desirable parameters. Under the calibration procedure, we compare the behaviour of model-free designs to model-based designs in a comprehensive simulation study, covering a number of clinically plausible scenarios. It is found that model-free designs are competitive with the model-based designs in terms of the proportion of correct selections of the maximum tolerated dose combination. However, there are a number of scenarios in which model-free designs offer a safer alternative. This is also illustrated in the application of the designs to a case study using data from a phase I oncology trial.
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Registered trials
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