ArticleStatistical methods in medical research2023
Local continual reassessment methods for dose finding and optimization in drug-combination trials.
Article in Statistical methods in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Applying the Partial Order Continual Reassessment Method to High-Dimensional Treatment Combinations.Statistics in medicine · 2026Article
- 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
- A randomized Bayesian phase I-II dose optimization design for combination cancer therapies with progression-free survival end point.BMC medical research methodology · 2025Article
- COMIC: A Bayesian Dose Optimization Design for Drug Combination in Multiple Indications With Application to CAR-T Therapies.Statistics in medicine · 2025Article
- Great Wall: A Generalized Dose Optimization Design for Drug Combination Trials Maximizing Survival Benefit.Pharmaceutical statisticsArticle
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Authors and funding
4 authors.
Funding
Abstract
Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.
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