ArticleStatistics in biopharmaceutical research2025
Comb-BOIN12: A Utility-Based Bayesian Optimal Interval Design for Dose Optimization in Cancer Drug-Combination Trials.
Article in Statistics in biopharmaceutical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- 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
- 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
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Authors and funding
4 authors.
Funding
Abstract
Drug combinations are increasingly utilized in cancer treatment to enhance drug effectiveness through synergistic therapeutic effects. However, determining the optimal biological dose combination (OBDC) in small-scale drug combination trials presents challenges due to the increased complexity of the dose space. To effectively optimize the dose combination of combined drugs, we propose a model-assisted design by extending the single-agent Bayesian optimal interval phase I/II (BOIN12) design. Our approach incorporates a utility function to balance the trade-off between risk and benefit and directly models the utility of each dose by constructing a quasi-beta-binomial model. A key advantage of our design is the simplification of decision-making during interim periods by considering all possible outcomes and pre-including the decision rule in the protocol. Additionally, we present a time-to-event (TITE) version of our design, employing an approximate likelihood approach to mitigate potential late-onset effects. We demonstrate that our proposed design exhibits robust and desirable operating characteristics across various scenarios through extensive simulation studies.
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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.